Diabetes 130-US hospitals for years 1999-2008
WILHEM CASTRO
11/29/2020
Diabetes 130-US hospitals for years 1999-2008
Los datos provienen del Centro de Investigación Clínica y Traslacional de la Virginia Commonwealth University. Reflejan Informacion de pacientes con diabetes en 130 hospitales de EE. UU. Durante un período de diez años desde 1999 hasta 2008. Hay más de 100.000 ingresos hospitalarios únicos en este conjunto de datos, de aproximadamente 70.000 pacientes únicos. Los datos incluyen elementos demográficos, como edad, sexo y raza, así como atributos clínicos como pruebas realizadas, visitas de emergencia
Objetivos - Identificar diferentes insights asociados a los típos de readmisión de los pacientes.
- Propones un modelo de clasificación, que permita predecir el típo de readmisión de los pacientes.
Metodología:
Se realizó una etapa de preparación de datos. Posteriormente se exploraron los datos de manera univariada y multivariada. Acto seguido, se realiza elproceso de feature engineering y se construye el set de entrenamiento y validación. Finalmente, se propone el modelo XGBoost como estrategia de clasificación.
Carga de Datos
#Data Loading----
ds<- read.csv('diabetic_data.csv')
dim(ds)## [1] 101766 50names(ds)## [1] "encounter_id" "patient_nbr"
## [3] "race" "gender"
## [5] "age" "weight"
## [7] "admission_type_id" "discharge_disposition_id"
## [9] "admission_source_id" "time_in_hospital"
## [11] "payer_code" "medical_specialty"
## [13] "num_lab_procedures" "num_procedures"
## [15] "num_medications" "number_outpatient"
## [17] "number_emergency" "number_inpatient"
## [19] "diag_1" "diag_2"
## [21] "diag_3" "number_diagnoses"
## [23] "max_glu_serum" "A1Cresult"
## [25] "metformin" "repaglinide"
## [27] "nateglinide" "chlorpropamide"
## [29] "glimepiride" "acetohexamide"
## [31] "glipizide" "glyburide"
## [33] "tolbutamide" "pioglitazone"
## [35] "rosiglitazone" "acarbose"
## [37] "miglitol" "troglitazone"
## [39] "tolazamide" "examide"
## [41] "citoglipton" "insulin"
## [43] "glyburide.metformin" "glipizide.metformin"
## [45] "glimepiride.pioglitazone" "metformin.rosiglitazone"
## [47] "metformin.pioglitazone" "change"
## [49] "diabetesMed" "readmitted"#head(ds)
dim(ds)## [1] 101766 50Se cuenta con 101766 registros y 50 variables
Datos Faltantes y manipulación de datos
#Data Preparation---
round(sapply(ds, function(x) sum(x=="?"))/101766*100,2) ## encounter_id patient_nbr race
## 0.00 0.00 2.23
## gender age weight
## 0.00 0.00 96.86
## admission_type_id discharge_disposition_id admission_source_id
## 0.00 0.00 0.00
## time_in_hospital payer_code medical_specialty
## 0.00 39.56 49.08
## num_lab_procedures num_procedures num_medications
## 0.00 0.00 0.00
## number_outpatient number_emergency number_inpatient
## 0.00 0.00 0.00
## diag_1 diag_2 diag_3
## 0.02 0.35 1.40
## number_diagnoses max_glu_serum A1Cresult
## 0.00 0.00 0.00
## metformin repaglinide nateglinide
## 0.00 0.00 0.00
## chlorpropamide glimepiride acetohexamide
## 0.00 0.00 0.00
## glipizide glyburide tolbutamide
## 0.00 0.00 0.00
## pioglitazone rosiglitazone acarbose
## 0.00 0.00 0.00
## miglitol troglitazone tolazamide
## 0.00 0.00 0.00
## examide citoglipton insulin
## 0.00 0.00 0.00
## glyburide.metformin glipizide.metformin glimepiride.pioglitazone
## 0.00 0.00 0.00
## metformin.rosiglitazone metformin.pioglitazone change
## 0.00 0.00 0.00
## diabetesMed readmitted
## 0.00 0.00ds<-ds[,c(-6,-11,-12)]
#str(ds)
dim(ds)## [1] 101766 47Como se puede observar existen campos con una alta proporción de datos faltantes, por lo que no se tendrán en cuenta en el análisis.
Estos campos son:
weight
payer
code
medical specialty
Por otro lado,existen campos 13 campos numéricos y 37 categóricos:
str(ds)## 'data.frame': 101766 obs. of 47 variables:
## $ encounter_id : int 2278392 149190 64410 500364 16680 35754 55842 63768 12522 15738 ...
## $ patient_nbr : int 8222157 55629189 86047875 82442376 42519267 82637451 84259809 114882984 48330783 63555939 ...
## $ race : Factor w/ 6 levels "?","AfricanAmerican",..: 4 4 2 4 4 4 4 4 4 4 ...
## $ gender : Factor w/ 3 levels "Female","Male",..: 1 1 1 2 2 2 2 2 1 1 ...
## $ age : Factor w/ 10 levels "[0-10)","[10-20)",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ admission_type_id : int 6 1 1 1 1 2 3 1 2 3 ...
## $ discharge_disposition_id: int 25 1 1 1 1 1 1 1 1 3 ...
## $ admission_source_id : int 1 7 7 7 7 2 2 7 4 4 ...
## $ time_in_hospital : int 1 3 2 2 1 3 4 5 13 12 ...
## $ num_lab_procedures : int 41 59 11 44 51 31 70 73 68 33 ...
## $ num_procedures : int 0 0 5 1 0 6 1 0 2 3 ...
## $ num_medications : int 1 18 13 16 8 16 21 12 28 18 ...
## $ number_outpatient : int 0 0 2 0 0 0 0 0 0 0 ...
## $ number_emergency : int 0 0 0 0 0 0 0 0 0 0 ...
## $ number_inpatient : int 0 0 1 0 0 0 0 0 0 0 ...
## $ diag_1 : Factor w/ 717 levels "?","10","11",..: 126 145 456 556 56 265 265 278 254 284 ...
## $ diag_2 : Factor w/ 749 levels "?","11","110",..: 1 81 80 99 26 248 248 316 262 48 ...
## $ diag_3 : Factor w/ 790 levels "?","11","110",..: 1 123 768 250 88 88 772 88 231 319 ...
## $ number_diagnoses : int 1 9 6 7 5 9 7 8 8 8 ...
## $ max_glu_serum : Factor w/ 4 levels ">200",">300",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ A1Cresult : Factor w/ 4 levels ">7",">8","None",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ metformin : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 3 2 2 2 ...
## $ repaglinide : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ nateglinide : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ chlorpropamide : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ glimepiride : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 3 2 2 2 ...
## $ acetohexamide : Factor w/ 2 levels "No","Steady": 1 1 1 1 1 1 1 1 1 1 ...
## $ glipizide : Factor w/ 4 levels "Down","No","Steady",..: 2 2 3 2 3 2 2 2 3 2 ...
## $ glyburide : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 2 3 2 2 ...
## $ tolbutamide : Factor w/ 2 levels "No","Steady": 1 1 1 1 1 1 1 1 1 1 ...
## $ pioglitazone : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ rosiglitazone : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 2 2 2 3 ...
## $ acarbose : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ miglitol : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ troglitazone : Factor w/ 2 levels "No","Steady": 1 1 1 1 1 1 1 1 1 1 ...
## $ tolazamide : Factor w/ 3 levels "No","Steady",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ examide : Factor w/ 1 level "No": 1 1 1 1 1 1 1 1 1 1 ...
## $ citoglipton : Factor w/ 1 level "No": 1 1 1 1 1 1 1 1 1 1 ...
## $ insulin : Factor w/ 4 levels "Down","No","Steady",..: 2 4 2 4 3 3 3 2 3 3 ...
## $ glyburide.metformin : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ glipizide.metformin : Factor w/ 2 levels "No","Steady": 1 1 1 1 1 1 1 1 1 1 ...
## $ glimepiride.pioglitazone: Factor w/ 2 levels "No","Steady": 1 1 1 1 1 1 1 1 1 1 ...
## $ metformin.rosiglitazone : Factor w/ 2 levels "No","Steady": 1 1 1 1 1 1 1 1 1 1 ...
## $ metformin.pioglitazone : Factor w/ 2 levels "No","Steady": 1 1 1 1 1 1 1 1 1 1 ...
## $ change : Factor w/ 2 levels "Ch","No": 2 1 2 1 1 2 1 2 1 1 ...
## $ diabetesMed : Factor w/ 2 levels "No","Yes": 1 2 2 2 2 2 2 2 2 2 ...
## $ readmitted : Factor w/ 3 levels "<30",">30","NO": 3 2 3 3 3 2 3 2 3 3 ...Teniendo en cuenta que algunos campos numéricos no aportan mucha información (por ejemplo el lugar codificado desde que los pacientes fueron dados de alta “discharge_disposition_id”), pero si lo pueden hacer como variables categóricas, se procede a redefinirlos como factores.
#Cambiar algunos numéricos a factor
ds$admission_source_id<- as.factor(ds$admission_source_id)
levels(ds$admission_source_id)## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "13" "14" "17" "20"
## [16] "22" "25"ds$admission_type_id<- as.factor(ds$admission_type_id)
levels(ds$admission_type_id)## [1] "1" "2" "3" "4" "5" "6" "7" "8"ds$discharge_disposition_id<-as.factor(ds$discharge_disposition_id)
levels(ds$discharge_disposition_id)## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15"
## [16] "16" "17" "18" "19" "20" "22" "23" "24" "25" "27" "28"Campos Numéricos
A continuación,se procesan los campos numéricos (excluyendo los Ids) para identificar sus características y su posible grado de asociación entre sí y a su vez con la variable repuesta (“Readmittion”). Recordando que puede tomar los siguientes valores:
A continuación, se muestran los gráficos de densidad y boxplot para estas variables. Posteriormene se realiza una prueba Tukey para explorar la significancia de cada una de ella respecto del tipo de readmisión.
Tiempo de estadía en el Hospital
##
## Descriptive statistics by group
## group: <30
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 11357 4.77 3.03 4 4.43 2.97 1 14 13 0.98 0.47 0.03
## ------------------------------------------------------------
## group: >30
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 35545 4.5 2.99 4 4.11 2.97 1 14 13 1.1 0.76 0.02
## ------------------------------------------------------------
## group: NO
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 54864 4.25 2.96 3 3.83 2.97 1 14 13 1.2 1.02 0.01## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = num$time_in_hospital ~ ds$readmitted)
##
## $`ds$readmitted`
## diff lwr upr p adj
## >30-<30 -0.2727078 -0.3479940 -0.1974216 0
## NO-<30 -0.5138195 -0.5858246 -0.4418145 0
## NO->30 -0.2411117 -0.2886686 -0.1935548 0Número de Exámenes de Laboratorio
##
## Descriptive statistics by group
## group: <30
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 11357 44.23 19.28 45 45 17.79 1 132 131 -0.28 -0.13
## se
## X1 0.18
## ------------------------------------------------------------
## group: >30
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 35545 43.84 19.57 45 44.64 19.27 1 129 128 -0.29 -0.22 0.1
## ------------------------------------------------------------
## group: NO
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 54864 42.38 19.8 44 42.97 19.27 1 126 125 -0.19 -0.27 0.08## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = num$num_lab_procedures ~ ds$readmitted)
##
## $`ds$readmitted`
## diff lwr upr p adj
## >30-<30 -0.3894265 -0.8860641 0.1072111 0.1573457
## NO-<30 -1.8444299 -2.3194230 -1.3694367 0.0000000
## NO->30 -1.4550034 -1.7687202 -1.1412865 0.0000000Número de Procedimientos
##
## Descriptive statistics by group
## group: <30
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 11357 1.28 1.64 1 0.96 1.48 0 6 6 1.38 1.17 0.02
## ------------------------------------------------------------
## group: >30
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 35545 1.25 1.67 1 0.92 1.48 0 6 6 1.41 1.16 0.01
## ------------------------------------------------------------
## group: NO
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 54864 1.41 1.74 1 1.09 1.48 0 6 6 1.24 0.63 0.01## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = num$num_procedures ~ ds$readmitted)
##
## $`ds$readmitted`
## diff lwr upr p adj
## >30-<30 -0.03128494 -0.07433457 0.0117647 0.2039427
## NO-<30 0.12942145 0.08824800 0.1705949 0.0000000
## NO->30 0.16070638 0.13351272 0.1879000 0.0000000Número de Medicamentos
##
## Descriptive statistics by group
## group: <30
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 11357 16.9 8.1 16 16.15 7.41 1 81 80 1.32 3.8 0.08
## ------------------------------------------------------------
## group: >30
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 35545 16.28 7.62 15 15.63 7.41 1 70 69 1.16 2.92 0.04
## ------------------------------------------------------------
## group: NO
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 54864 15.67 8.43 14 14.78 7.41 1 79 78 1.42 3.66 0.04## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = num$num_medications ~ ds$readmitted)
##
## $`ds$readmitted`
## diff lwr upr p adj
## >30-<30 -0.6203751 -0.8254246 -0.4153256 0
## NO-<30 -1.2327767 -1.4288897 -1.0366637 0
## NO->30 -0.6124016 -0.7419276 -0.4828756 0Número de Tratamientos Ambulatorios
##
## Descriptive statistics by group
## group: <30
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 11357 0.44 1.3 0 0.13 0 0 40 40 7.51 118.93 0.01
## ------------------------------------------------------------
## group: >30
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 35545 0.5 1.54 0 0.15 0 0 42 42 8.41 126.25 0.01
## ------------------------------------------------------------
## group: NO
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 54864 0.27 1.03 0 0.03 0 0 36 36 8.67 143.68 0## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = num$number_outpatient ~ ds$readmitted)
##
## $`ds$readmitted`
## diff lwr upr p adj
## >30-<30 0.05941744 0.02751399 0.0913209 3.78e-05
## NO-<30 -0.16379946 -0.19431250 -0.1332864 0.00e+00
## NO->30 -0.22321690 -0.24336973 -0.2030641 0.00e+00Número de Atenciones de Urgencia
##
## Descriptive statistics by group
## group: <30
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 11357 0.36 1.37 0 0.08 0 0 64 64 14.69 475.65 0.01
## ------------------------------------------------------------
## group: >30
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 35545 0.28 1.19 0 0.06 0 0 76 76 21.23 937.14 0.01
## ------------------------------------------------------------
## group: NO
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 54864 0.11 0.52 0 0 0 0 37 37 17.39 757.67 0## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = num$number_emergency ~ ds$readmitted)
##
## $`ds$readmitted`
## diff lwr upr p adj
## >30-<30 -0.07364408 -0.09701908 -0.05026908 0
## NO-<30 -0.24809716 -0.27045343 -0.22574089 0
## NO->30 -0.17445307 -0.18921863 -0.15968752 0Número de Hospitalizaciones
##
## Descriptive statistics by group
## group: <30
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 11357 1.22 1.95 0 0.79 0 0 21 21 2.79 10.98 0.02
## ------------------------------------------------------------
## group: >30
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 35545 0.84 1.39 0 0.53 0 0 19 19 2.92 13.58 0.01
## ------------------------------------------------------------
## group: NO
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 54864 0.38 0.86 0 0.18 0 0 16 16 3.78 23.99 0## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = num$number_inpatient ~ ds$readmitted)
##
## $`ds$readmitted`
## diff lwr upr p adj
## >30-<30 -0.3850100 -0.4160231 -0.3539969 0
## NO-<30 -0.8420401 -0.8717016 -0.8123787 0
## NO->30 -0.4570302 -0.4766206 -0.4374397 0Número de Diagnósticos
##
## Descriptive statistics by group
## group: <30
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 11357 7.69 1.77 9 7.98 0 1 16 15 -1.08 0.46 0.02
## ------------------------------------------------------------
## group: >30
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 35545 7.65 1.81 9 7.95 0 1 16 15 -1.08 0.44 0.01
## ------------------------------------------------------------
## group: NO
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 54864 7.22 2.02 8 7.48 1.48 1 16 15 -0.71 -0.4 0.01## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = num$number_diagnoses ~ ds$readmitted)
##
## $`ds$readmitted`
## diff lwr upr p adj
## >30-<30 -0.04589029 -0.09442676 0.002646182 0.0685035
## NO-<30 -0.47142303 -0.51784418 -0.425001877 0.0000000
## NO->30 -0.42553274 -0.45619234 -0.394873143 0.0000000Se puede observar que en general todas las variables numéricas que describen el uso que el paciente le ha dado al sistema (costos variables) parecen ser buenos estimadores para determinar las readmisiones.
Asociaciones entre Campos Numéricos
No hay asociaciones muy fuertes entre las variables, con excepción del tiempo de hospitalización y el número de medicamentos, asociación fácilmente comprensible.
Previo al procesamiento de las variables categóricas, se muestra gráficamente el comportamiento de todos los campos numéricos respecto de los tipos de readmisión y se realiza una estandarización de todos ellos.
## [1] "<30" ">30" "NO"
## vars n mean sd median trimmed mad min max range
## time_in_hospital 1 101766 0 1 -0.13 -0.13 0.99 -1.14 3.22 4.35
## num_lab_procedures 2 101766 0 1 0.05 0.04 0.98 -2.14 4.52 6.66
## num_procedures 3 101766 0 1 -0.20 -0.19 0.87 -0.79 2.73 3.52
## num_medications 4 101766 0 1 -0.13 -0.10 0.91 -1.85 7.99 9.84
## number_outpatient 5 101766 0 1 -0.29 -0.23 0.00 -0.29 32.85 33.14
## number_emergency 6 101766 0 1 -0.21 -0.20 0.00 -0.21 81.47 81.68
## number_inpatient 7 101766 0 1 -0.50 -0.23 0.00 -0.50 16.13 16.63
## number_diagnoses 8 101766 0 1 0.30 0.15 0.77 -3.32 4.44 7.76
## skew kurtosis se
## time_in_hospital 1.13 0.85 0
## num_lab_procedures -0.24 -0.25 0
## num_procedures 1.32 0.86 0
## num_medications 1.33 3.47 0
## number_outpatient 8.83 147.90 0
## number_emergency 22.85 1191.60 0
## number_inpatient 3.61 20.72 0
## number_diagnoses -0.88 -0.08 0Variables Categóricas
Ahora, se contruyen las 36 tablas cruzadas correspondiantes a las asociaciones entre todas las variables categóricas y la variable respuesta(“readmittion”). También, se realizan pruebas chi-squared para estimar la significancia entre cada variable categórica y la variable respuesta(“readmitted”). Lo anterior nos ayuda a tener una idea de qué variables aportarían o no en el proceso de clasificación.
A continuación, se muestran las tablas sólo como ejemplo ilustrativo, pues algunos campos de estos tienen muchos niveles(36 tablas).
#Categóricos----
fact_cols <- unlist(lapply(ds, is.factor)) # Categoricos
fact<-ds[,fact_cols]
names(fact)## [1] "race" "gender"
## [3] "age" "admission_type_id"
## [5] "discharge_disposition_id" "admission_source_id"
## [7] "diag_1" "diag_2"
## [9] "diag_3" "max_glu_serum"
## [11] "A1Cresult" "metformin"
## [13] "repaglinide" "nateglinide"
## [15] "chlorpropamide" "glimepiride"
## [17] "acetohexamide" "glipizide"
## [19] "glyburide" "tolbutamide"
## [21] "pioglitazone" "rosiglitazone"
## [23] "acarbose" "miglitol"
## [25] "troglitazone" "tolazamide"
## [27] "examide" "citoglipton"
## [29] "insulin" "glyburide.metformin"
## [31] "glipizide.metformin" "glimepiride.pioglitazone"
## [33] "metformin.rosiglitazone" "metformin.pioglitazone"
## [35] "change" "diabetesMed"
## [37] "readmitted"dim(fact)## [1] 101766 37prop.table(table(fact$readmitted))# Hay un pequeño desbalance para <30##
## <30 >30 NO
## 0.1115992 0.3492817 0.5391192str(fact)## 'data.frame': 101766 obs. of 37 variables:
## $ race : Factor w/ 6 levels "?","AfricanAmerican",..: 4 4 2 4 4 4 4 4 4 4 ...
## $ gender : Factor w/ 3 levels "Female","Male",..: 1 1 1 2 2 2 2 2 1 1 ...
## $ age : Factor w/ 10 levels "[0-10)","[10-20)",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ admission_type_id : Factor w/ 8 levels "1","2","3","4",..: 6 1 1 1 1 2 3 1 2 3 ...
## $ discharge_disposition_id: Factor w/ 26 levels "1","2","3","4",..: 24 1 1 1 1 1 1 1 1 3 ...
## $ admission_source_id : Factor w/ 17 levels "1","2","3","4",..: 1 7 7 7 7 2 2 7 4 4 ...
## $ diag_1 : Factor w/ 717 levels "?","10","11",..: 126 145 456 556 56 265 265 278 254 284 ...
## $ diag_2 : Factor w/ 749 levels "?","11","110",..: 1 81 80 99 26 248 248 316 262 48 ...
## $ diag_3 : Factor w/ 790 levels "?","11","110",..: 1 123 768 250 88 88 772 88 231 319 ...
## $ max_glu_serum : Factor w/ 4 levels ">200",">300",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ A1Cresult : Factor w/ 4 levels ">7",">8","None",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ metformin : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 3 2 2 2 ...
## $ repaglinide : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ nateglinide : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ chlorpropamide : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ glimepiride : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 3 2 2 2 ...
## $ acetohexamide : Factor w/ 2 levels "No","Steady": 1 1 1 1 1 1 1 1 1 1 ...
## $ glipizide : Factor w/ 4 levels "Down","No","Steady",..: 2 2 3 2 3 2 2 2 3 2 ...
## $ glyburide : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 2 3 2 2 ...
## $ tolbutamide : Factor w/ 2 levels "No","Steady": 1 1 1 1 1 1 1 1 1 1 ...
## $ pioglitazone : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ rosiglitazone : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 2 2 2 3 ...
## $ acarbose : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ miglitol : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ troglitazone : Factor w/ 2 levels "No","Steady": 1 1 1 1 1 1 1 1 1 1 ...
## $ tolazamide : Factor w/ 3 levels "No","Steady",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ examide : Factor w/ 1 level "No": 1 1 1 1 1 1 1 1 1 1 ...
## $ citoglipton : Factor w/ 1 level "No": 1 1 1 1 1 1 1 1 1 1 ...
## $ insulin : Factor w/ 4 levels "Down","No","Steady",..: 2 4 2 4 3 3 3 2 3 3 ...
## $ glyburide.metformin : Factor w/ 4 levels "Down","No","Steady",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ glipizide.metformin : Factor w/ 2 levels "No","Steady": 1 1 1 1 1 1 1 1 1 1 ...
## $ glimepiride.pioglitazone: Factor w/ 2 levels "No","Steady": 1 1 1 1 1 1 1 1 1 1 ...
## $ metformin.rosiglitazone : Factor w/ 2 levels "No","Steady": 1 1 1 1 1 1 1 1 1 1 ...
## $ metformin.pioglitazone : Factor w/ 2 levels "No","Steady": 1 1 1 1 1 1 1 1 1 1 ...
## $ change : Factor w/ 2 levels "Ch","No": 2 1 2 1 1 2 1 2 1 1 ...
## $ diabetesMed : Factor w/ 2 levels "No","Yes": 1 2 2 2 2 2 2 2 2 2 ...
## $ readmitted : Factor w/ 3 levels "<30",">30","NO": 3 2 3 3 3 2 3 2 3 3 ...names(fact)## [1] "race" "gender"
## [3] "age" "admission_type_id"
## [5] "discharge_disposition_id" "admission_source_id"
## [7] "diag_1" "diag_2"
## [9] "diag_3" "max_glu_serum"
## [11] "A1Cresult" "metformin"
## [13] "repaglinide" "nateglinide"
## [15] "chlorpropamide" "glimepiride"
## [17] "acetohexamide" "glipizide"
## [19] "glyburide" "tolbutamide"
## [21] "pioglitazone" "rosiglitazone"
## [23] "acarbose" "miglitol"
## [25] "troglitazone" "tolazamide"
## [27] "examide" "citoglipton"
## [29] "insulin" "glyburide.metformin"
## [31] "glipizide.metformin" "glimepiride.pioglitazone"
## [33] "metformin.rosiglitazone" "metformin.pioglitazone"
## [35] "change" "diabetesMed"
## [37] "readmitted"for (i in seq(1,26)){
print(i)
print((names(fact[i])))
print(round(prop.table(table(fact$readmitted,fact[,i]),1)*100,1))
print(chisq.test(fact$readmitted,fact[,i]))#Ho: Independence
}## [1] 1
## [1] "race"
##
## ? AfricanAmerican Asian Caucasian Hispanic Other
## <30 1.7 19.0 0.6 75.7 1.9 1.3
## >30 1.5 18.7 0.5 76.3 1.8 1.3
## NO 2.8 19.0 0.8 73.6 2.2 1.7
##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 282.59, df = 10, p-value < 2.2e-16
##
## [1] 2
## [1] "gender"
##
## Female Male Unknown/Invalid
## <30 54.2 45.8 0.0
## >30 54.9 45.1 0.0
## NO 52.9 47.1 0.0## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 37.461, df = 4, p-value = 1.447e-07
##
## [1] 3
## [1] "age"
##
## [0-10) [10-20) [20-30) [30-40) [40-50) [50-60) [60-70) [70-80) [80-90)
## <30 0.0 0.4 2.1 3.7 9.0 14.7 22.0 27.0 18.3
## >30 0.1 0.6 1.4 3.3 9.2 16.6 22.2 26.7 17.5
## NO 0.2 0.8 1.7 3.9 9.8 17.6 22.0 24.7 16.2
##
## [90-100)
## <30 2.7
## >30 2.3
## NO 3.1
##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 313.17, df = 18, p-value < 2.2e-16
##
## [1] 4
## [1] "admission_type_id"
##
## 1 2 3 4 5 6 7 8
## <30 54.8 18.2 17.3 0.0 4.4 5.2 0.0 0.2
## >30 54.3 18.2 16.2 0.0 4.8 6.3 0.0 0.2
## NO 51.9 18.2 20.3 0.0 4.7 4.5 0.0 0.4## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 415.76, df = 14, p-value < 2.2e-16
##
## [1] 5
## [1] "discharge_disposition_id"
##
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## <30 49.3 3.0 18.0 0.9 2.2 14.4 0.8 0.1 0.1 0.0 0.0 0.0 0.2 0.2
## >30 60.5 1.9 13.8 0.8 1.0 15.1 0.6 0.1 0.0 0.0 0.0 0.0 0.1 0.0
## NO 60.4 2.0 12.7 0.8 1.1 10.8 0.6 0.1 0.0 0.0 3.0 0.0 0.6 0.6
##
## 15 16 17 18 19 20 22 23 24 25 27 28
## <30 0.2 0.0 0.0 4.0 0.0 0.0 4.9 0.3 0.1 0.8 0.0 0.4
## >30 0.1 0.0 0.0 2.9 0.0 0.0 1.5 0.4 0.0 1.1 0.0 0.1
## NO 0.0 0.0 0.0 4.0 0.0 0.0 1.7 0.4 0.0 0.9 0.0 0.1## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 3587.3, df = 50, p-value < 2.2e-16
##
## [1] 6
## [1] "admission_source_id"
##
## 1 2 3 4 5 6 7 8 9 10 11 13 14 17
## <30 27.6 1.0 0.3 2.7 0.9 1.9 59.2 0.0 0.1 0.0 0.0 0.0 0.0 6.2
## >30 27.1 0.9 0.2 1.9 0.7 1.1 61.0 0.0 0.0 0.0 0.0 0.0 0.0 6.9
## NO 30.6 1.2 0.2 4.0 0.9 3.0 53.1 0.0 0.2 0.0 0.0 0.0 0.0 6.6
##
## 20 22 25
## <30 0.2 0.0 0.0
## >30 0.2 0.0 0.0
## NO 0.1 0.0 0.0## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 1151, df = 32, p-value < 2.2e-16
##
## [1] 7
## [1] "diag_1"
##
## ? 10 11 110 112 114 115 117 131 133 135 136 141 142 143 145 146 147
## <30 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 148 149 150 151 152 153 154 155 156 157 158 160 161 162 163 164 170 171
## <30 0.0 0.0 0.0 0.0 0.0 0.3 0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.3 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.1 0.0 0.3 0.1 0.1 0.0 0.2 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0
##
## 172 173 174 175 179 180 182 183 184 185 187 188 189 191 192 193 194 195
## <30 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.2 0.0 0.0 0.0 0.2 0.0 0.0 0.2 0.0 0.1 0.1 0.1 0.0 0.0 0.0 0.0
##
## 196 197 198 199 200 201 202 203 204 205 207 208 210 211 212 214 215 216
## <30 0.0 0.4 0.3 0.0 0.0 0.0 0.2 0.0 0.1 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0
## >30 0.0 0.2 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0
## NO 0.0 0.4 0.3 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0
##
## 217 218 219 220 223 225 226 227 228 229 23 230 233 235 236 237 238 239
## <30 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1
## >30 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0
## NO 0.0 0.2 0.0 0.1 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0
##
## 240 241 242 244 245 246 250 250.01 250.02 250.03 250.1 250.11 250.12
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.5 0.1 0.3 0.8 0.3
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.6 0.1 0.3 0.7 0.4
## NO 0.0 0.1 0.0 0.0 0.0 0.0 0.3 0.1 0.7 0.3 0.3 0.5 0.4
##
## 250.13 250.2 250.21 250.22 250.23 250.3 250.31 250.32 250.33 250.4 250.41
## <30 1.0 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.2 0.2
## >30 0.8 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.4 0.1
## NO 0.8 0.1 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.2 0.1
##
## 250.42 250.43 250.5 250.51 250.52 250.53 250.6 250.7 250.8 250.81 250.82
## <30 0.2 0.1 0.0 0.0 0.0 0.0 1.9 1.5 1.6 0.2 0.6
## >30 0.1 0.0 0.0 0.0 0.0 0.0 1.5 0.9 2.0 0.2 0.5
## NO 0.1 0.0 0.0 0.0 0.0 0.0 0.8 0.7 1.5 0.2 0.3
##
## 250.83 250.9 250.91 250.92 250.93 251 252 253 255 261 262 263 266 27 271
## <30 0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289
## <30 0.0 0.0 0.1 0.0 2.3 0.0 0.2 0.0 0.3 0.1 0.1 0.0 0.1 0.5 0.0 0.1 0.1 0.0
## >30 0.0 0.0 0.1 0.0 2.0 0.1 0.1 0.0 0.4 0.0 0.1 0.0 0.1 0.4 0.0 0.1 0.1 0.0
## NO 0.0 0.0 0.1 0.1 1.7 0.0 0.6 0.0 0.3 0.0 0.0 0.0 0.0 0.3 0.0 0.1 0.1 0.0
##
## 290 291 292 293 294 295 296 297 298 299 3 300 301 303 304 305 306 307
## <30 0.1 0.1 0.1 0.1 0.2 0.4 0.9 0.0 0.1 0.0 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.0
## >30 0.0 0.1 0.1 0.1 0.1 0.5 0.9 0.0 0.0 0.0 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.0
## NO 0.1 0.1 0.1 0.0 0.1 0.4 0.9 0.0 0.1 0.0 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.0
##
## 308 309 31 310 311 312 314 318 320 322 323 324 325 327 331 332 333 334
## <30 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.1 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0
##
## 335 336 337 338 34 340 341 342 344 345 346 347 348 349 35 350 351 352
## <30 0.0 0.0 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.2 0.0 0.0 0.0 0.0 0.0
##
## 353 354 355 356 357 358 359 36 360 361 362 363 365 366 368 369 370 372
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 373 374 375 376 377 378 379 38 380 381 382 383 384 385 386 388 389 39
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.6 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.7 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0
##
## 391 394 395 396 397 398 401 402 403 404 405 41 410 411 412 413 414 415
## <30 0.0 0.0 0.0 0.0 0.0 0.1 0.3 0.4 0.7 0.3 0.0 0.0 3.3 0.2 0.0 0.1 5.2 0.4
## >30 0.0 0.0 0.0 0.0 0.0 0.2 0.4 0.5 0.6 0.3 0.0 0.0 3.0 0.3 0.0 0.1 6.0 0.4
## NO 0.0 0.0 0.0 0.0 0.0 0.1 0.3 0.4 0.4 0.2 0.0 0.0 4.0 0.3 0.0 0.1 7.0 0.5
##
## 416 417 42 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434
## <30 0.0 0.0 0.0 0.1 0.1 0.0 0.1 0.2 0.1 0.2 2.2 8.5 0.1 0.0 0.1 0.1 0.7 2.9
## >30 0.1 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.1 0.2 2.7 8.7 0.0 0.0 0.1 0.0 0.8 1.7
## NO 0.1 0.0 0.0 0.1 0.0 0.0 0.1 0.2 0.1 0.3 2.8 5.1 0.0 0.0 0.2 0.1 0.8 2.0
##
## 435 436 437 438 440 441 442 443 444 445 446 447 448 451 452 453 454 455
## <30 0.7 0.2 0.1 0.1 1.2 0.1 0.0 0.2 0.2 0.0 0.0 0.1 0.0 0.0 0.0 0.5 0.0 0.1
## >30 1.0 0.2 0.1 0.1 0.9 0.1 0.0 0.1 0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.5 0.0 0.1
## NO 1.0 0.2 0.1 0.1 0.7 0.1 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.1
##
## 456 457 458 459 461 462 463 464 465 466 47 470 471 473 474 475 477 478
## <30 0.0 0.0 0.4 0.1 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.5 0.1 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.4 0.1 0.0 0.0 0.0 0.0 0.1 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 48 480 481 482 483 485 486 487 49 490 491 492 493 494 495 496 5 500
## <30 0.0 0.0 0.0 0.4 0.0 0.0 2.8 0.1 0.0 0.1 2.5 0.0 0.9 0.0 0.0 0.1 0.0 0.0
## >30 0.0 0.0 0.1 0.4 0.0 0.0 3.9 0.1 0.0 0.1 3.0 0.0 1.4 0.0 0.0 0.1 0.0 0.0
## NO 0.0 0.0 0.1 0.3 0.0 0.0 3.3 0.1 0.0 0.1 1.7 0.0 0.8 0.0 0.0 0.1 0.0 0.0
##
## 501 506 507 508 510 511 512 513 514 515 516 518 519 52 521 522 523 524
## <30 0.0 0.0 0.8 0.0 0.0 0.4 0.1 0.0 0.1 0.1 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.4 0.0 0.0 0.2 0.0 0.0 0.0 0.1 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.7 0.0 0.0 0.3 0.0 0.0 0.0 0.1 0.0 1.2 0.1 0.0 0.0 0.0 0.0 0.0
##
## 526 527 528 529 53 530 531 532 533 534 535 536 537 54 540 541 542 543
## <30 0.0 0.0 0.0 0.0 0.1 0.4 0.2 0.1 0.0 0.0 0.3 0.2 0.2 0.0 0.1 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.1 0.5 0.3 0.2 0.0 0.0 0.5 0.2 0.1 0.0 0.1 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.1 0.6 0.3 0.2 0.0 0.0 0.4 0.1 0.1 0.0 0.3 0.0 0.0 0.0
##
## 550 551 552 553 555 556 557 558 560 562 564 565 566 567 568 569 57 570
## <30 0.1 0.0 0.2 0.1 0.0 0.0 0.2 0.4 0.8 0.8 0.1 0.0 0.0 0.1 0.0 0.2 0.0 0.0
## >30 0.0 0.0 0.2 0.1 0.0 0.0 0.2 0.4 0.9 1.0 0.1 0.0 0.1 0.1 0.0 0.3 0.0 0.0
## NO 0.1 0.0 0.3 0.2 0.1 0.1 0.2 0.3 0.8 1.0 0.1 0.0 0.1 0.0 0.0 0.3 0.0 0.0
##
## 571 572 573 574 575 576 577 578 579 58 580 581 582 583 584 585 586 588
## <30 0.4 0.4 0.0 0.7 0.2 0.1 1.3 0.6 0.0 0.0 0.0 0.0 0.0 0.0 1.8 0.1 0.0 0.0
## >30 0.2 0.3 0.0 0.8 0.2 0.1 1.1 0.7 0.0 0.0 0.0 0.0 0.0 0.0 1.5 0.1 0.0 0.0
## NO 0.3 0.2 0.0 1.1 0.2 0.1 0.9 0.6 0.0 0.0 0.0 0.0 0.0 0.0 1.4 0.1 0.0 0.0
##
## 590 591 592 593 594 595 596 598 599 600 601 602 603 604 605 607 608 61
## <30 0.2 0.0 0.3 0.2 0.0 0.1 0.0 0.0 1.5 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0
## >30 0.3 0.0 0.3 0.1 0.0 0.1 0.1 0.0 1.7 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0
## NO 0.4 0.0 0.4 0.1 0.0 0.1 0.0 0.0 1.5 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0
##
## 610 611 614 615 616 617 618 619 620 621 622 623 625 626 627 632 633 634
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.1 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0
##
## 637 640 641 642 643 644 645 646 647 648 649 652 653 654 655 656 657 658
## <30 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0
##
## 659 66 660 661 663 664 665 669 671 674 680 681 682 683 684 685 686 690
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 1.6 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 2.2 0.0 0.0 0.0 0.0 0.0
## NO 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 2.0 0.0 0.0 0.0 0.0 0.0
##
## 691 692 693 694 695 696 698 7 70 700 703 704 705 706 707 708 709 710
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0
##
## 711 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730
## <30 0.1 0.1 1.9 0.0 0.0 0.0 0.1 0.0 0.1 0.4 0.0 0.4 0.0 0.0 0.0 0.1 0.1 0.3
## >30 0.1 0.0 1.6 0.0 0.0 0.0 0.1 0.0 0.1 0.5 0.1 0.4 0.0 0.1 0.0 0.1 0.1 0.3
## NO 0.1 0.0 2.5 0.1 0.0 0.0 0.0 0.0 0.2 1.0 0.1 0.5 0.0 0.1 0.1 0.1 0.1 0.2
##
## 731 732 733 734 735 736 737 738 745 746 747 75 751 753 756 759 78 780
## <30 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.7
## >30 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.1
## NO 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0
##
## 781 782 783 784 785 786 787 788 789 79 790 791 792 793 794 795 796 797
## <30 0.1 0.1 0.0 0.1 0.1 2.6 0.4 0.1 0.6 0.1 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.1 0.1 0.0 0.2 0.1 4.0 0.3 0.0 0.6 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.1 0.1 0.0 0.1 0.1 4.2 0.2 0.0 0.5 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 799 8 800 801 802 803 804 805 806 807 808 810 812 813 814 815 816 817
## <30 0.1 0.6 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.1 0.2 0.0 0.2 0.1 0.0 0.0 0.0 0.0
## >30 0.1 0.5 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.1 0.1 0.0 0.2 0.0 0.0 0.0 0.0 0.0
## NO 0.1 0.5 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.1 0.1 0.0 0.3 0.1 0.0 0.0 0.0 0.0
##
## 82 820 821 822 823 824 825 826 827 831 832 833 834 835 836 837 838 839
## <30 0.0 1.5 0.1 0.0 0.1 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.8 0.1 0.0 0.1 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 1.1 0.2 0.1 0.1 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 84 840 842 843 844 845 846 847 848 850 851 852 853 854 860 861 862 863
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0
##
## 864 865 866 867 868 870 871 873 875 878 879 88 880 881 882 883 885 886
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 890 891 892 893 895 897 9 903 904 906 911 913 914 915 916 917 919 920
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 921 922 923 924 928 933 934 935 936 939 94 941 942 944 945 952 955 957
## <30 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 958 959 962 963 964 965 966 967 968 969 97 970 971 972 973 974 975 976
## <30 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 977 98 980 982 983 986 987 988 989 990 991 992 994 995 996 997 998 999
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 2.3 0.4 0.8 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 2.2 0.4 0.7 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 1.7 0.4 0.8 0.0
##
## E909 V07 V25 V26 V43 V45 V51 V53 V54 V55 V56 V57 V58 V60 V63 V66 V67 V70
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.4 0.8 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 1.2 0.2 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.1 0.0 1.1 0.1 0.0 0.0 0.0 0.0 0.0
##
## V71
## <30 0.0
## >30 0.0
## NO 0.0## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 4970.8, df = 1432, p-value < 2.2e-16
##
## [1] 8
## [1] "diag_2"
##
## ? 11 110 111 112 114 115 117 123 130 131 135 136 137 138 140 141 145
## <30 0.3 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.2 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.5 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0
##
## 150 151 152 153 154 155 156 157 162 163 164 171 172 173 174 179 180 182
## <30 0.1 0.0 0.1 0.2 0.1 0.0 0.0 0.1 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.1 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 183 185 186 188 189 191 192 193 195 196 197 198 199 200 201 202 203 204
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.5 0.4 0.0 0.0 0.0 0.4 0.2 0.1
## >30 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.2 0.3 0.2 0.0 0.0 0.0 0.2 0.1 0.1
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.5 0.3 0.0 0.0 0.0 0.1 0.1 0.1
##
## 205 208 211 212 214 215 217 218 220 223 225 226 227 228 232 233 235 238
## <30 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 239 240 241 242 244 245 246 250 250.01 250.02 250.03 250.1 250.11 250.12
## <30 0.0 0.0 0.0 0.1 0.1 0.0 0.0 3.9 1.7 1.9 0.3 0.1 0.1 0.1
## >30 0.0 0.0 0.0 0.1 0.1 0.0 0.0 4.7 1.5 2.1 0.3 0.0 0.1 0.1
## NO 0.0 0.0 0.0 0.1 0.2 0.0 0.0 7.2 1.5 2.0 0.2 0.0 0.1 0.1
##
## 250.13 250.2 250.21 250.22 250.23 250.3 250.31 250.32 250.33 250.4 250.41
## <30 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.5
## >30 0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.2 0.3
## NO 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1
##
## 250.42 250.43 250.5 250.51 250.52 250.53 250.6 250.7 250.8 250.81 250.82
## <30 0.1 0.1 0.1 0.1 0.1 0.1 1.3 0.2 0.2 0.1 0.2
## >30 0.1 0.0 0.1 0.1 0.1 0.0 1.0 0.1 0.2 0.1 0.2
## NO 0.1 0.0 0.1 0.1 0.0 0.0 0.7 0.1 0.2 0.1 0.1
##
## 250.83 250.9 250.91 250.92 250.93 251 252 253 255 256 258 259 260 261 262
## <30 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.1 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0
##
## 263 266 268 269 27 270 271 272 273 274 275 276 277 278 279 280 281 282
## <30 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 7.1 0.0 0.1 0.0 0.6 0.0 0.0
## >30 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 6.7 0.1 0.2 0.0 0.6 0.0 0.1
## NO 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 6.5 0.0 0.3 0.0 0.6 0.0 0.0
##
## 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300
## <30 0.0 0.1 1.2 0.1 0.3 0.1 0.0 0.1 0.0 0.1 0.1 0.1 0.3 0.2 0.0 0.0 0.0 0.1
## >30 0.0 0.2 1.4 0.1 0.3 0.1 0.1 0.0 0.1 0.1 0.1 0.1 0.4 0.1 0.0 0.0 0.0 0.2
## NO 0.0 0.2 1.6 0.1 0.3 0.1 0.0 0.1 0.1 0.1 0.1 0.2 0.4 0.2 0.0 0.0 0.0 0.1
##
## 301 302 303 304 305 306 307 308 309 31 310 311 312 314 316 317 318 319
## <30 0.0 0.0 0.3 0.2 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.1 0.0 0.3 0.2 0.6 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.1 0.0 0.3 0.1 0.8 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0
##
## 320 322 323 324 325 327 331 332 333 335 336 337 338 34 340 341 342 343
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.4 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.1 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.2 0.0
##
## 344 345 346 347 348 349 35 350 351 352 353 354 355 356 357 358 359 360
## <30 0.0 0.1 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0
## >30 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0
## NO 0.1 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0
##
## 362 364 365 366 368 369 372 373 374 376 377 378 379 38 380 381 382 383
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0
##
## 386 388 389 394 395 396 397 398 40 401 402 403 404 405 41 410 411 412
## <30 0.0 0.0 0.0 0.0 0.0 0.2 0.1 0.0 0.0 2.4 0.3 3.8 0.2 0.0 0.3 0.6 2.2 0.1
## >30 0.0 0.0 0.0 0.0 0.0 0.2 0.1 0.0 0.0 2.9 0.3 3.6 0.3 0.0 0.4 0.5 2.6 0.1
## NO 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.0 4.4 0.3 2.0 0.2 0.0 0.4 0.6 2.5 0.1
##
## 413 414 415 416 42 420 421 422 423 424 425 426 427 428 429 430 431 432
## <30 0.8 1.9 0.1 0.1 0.1 0.0 0.0 0.0 0.0 1.0 1.5 0.2 5.0 7.4 0.0 0.0 0.0 0.0
## >30 1.1 2.5 0.1 0.1 0.1 0.0 0.0 0.0 0.0 1.1 1.7 0.3 5.1 7.5 0.0 0.0 0.0 0.0
## NO 1.1 2.8 0.1 0.1 0.1 0.0 0.0 0.0 0.0 1.0 1.2 0.3 4.8 5.7 0.0 0.0 0.0 0.0
##
## 433 434 435 436 437 438 440 441 442 443 444 446 447 448 451 452 453 454
## <30 0.2 0.2 0.1 0.0 0.1 0.3 0.7 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.4 0.0
## >30 0.1 0.1 0.1 0.0 0.0 0.3 0.4 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.3 0.0
## NO 0.1 0.2 0.1 0.0 0.1 0.2 0.3 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.4 0.0
##
## 455 456 457 458 459 46 460 461 462 463 464 465 466 470 472 473 474 475
## <30 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.1 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.1 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0
##
## 477 478 480 481 482 483 484 485 486 487 490 491 492 493 494 495 496 5
## <30 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 1.3 0.0 0.0 1.8 0.2 0.6 0.0 0.0 3.2 0.0
## >30 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 1.4 0.0 0.1 2.0 0.2 0.9 0.0 0.0 3.8 0.0
## NO 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 1.4 0.0 0.0 1.2 0.2 0.9 0.0 0.0 2.9 0.0
##
## 500 501 506 507 508 510 511 512 513 514 515 516 517 518 519 52 520 521
## <30 0.0 0.0 0.0 0.1 0.0 0.0 0.6 0.0 0.0 0.0 0.1 0.0 0.0 1.2 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.1 0.0 0.0 0.5 0.0 0.0 0.0 0.2 0.0 0.0 1.1 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.2 0.0 0.0 0.5 0.1 0.0 0.0 0.2 0.0 0.0 1.5 0.0 0.0 0.0 0.0
##
## 522 523 524 527 528 529 53 530 531 532 533 534 535 536 537 54 540 542
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.1 0.0 0.0 0.0 0.2 0.3 0.1 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.2 0.3 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.1 0.0 0.0 0.0 0.2 0.1 0.0 0.0 0.0 0.0
##
## 543 550 552 553 555 556 557 558 560 562 564 565 566 567 568 569 570 571
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.4 0.1 0.1 0.0 0.0 0.1 0.0 0.1 0.0 0.6
## >30 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.3 0.1 0.1 0.0 0.0 0.1 0.1 0.2 0.0 0.5
## NO 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.1 0.5 0.2 0.1 0.0 0.0 0.1 0.1 0.2 0.0 0.3
##
## 572 573 574 575 576 577 578 579 580 581 583 584 585 586 588 590 591 592
## <30 0.2 0.1 0.2 0.0 0.1 0.6 0.3 0.0 0.0 0.1 0.0 1.7 2.4 0.0 0.0 0.1 0.2 0.1
## >30 0.1 0.0 0.3 0.1 0.1 0.4 0.3 0.0 0.0 0.1 0.0 1.6 2.3 0.0 0.0 0.1 0.2 0.1
## NO 0.1 0.0 0.4 0.1 0.1 0.3 0.4 0.0 0.0 0.1 0.0 1.6 1.4 0.0 0.0 0.1 0.3 0.1
##
## 593 594 595 596 598 599 600 601 602 603 604 605 607 608 610 611 614 615
## <30 0.1 0.0 0.1 0.1 0.0 3.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.1 0.0 0.1 0.0 0.0 3.3 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.1 0.0 0.1 0.1 0.0 3.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 616 617 618 619 620 621 622 623 625 626 627 634 641 642 644 645 646 647
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 648 649 652 654 656 658 659 66 661 663 664 665 670 674 680 681 682 683
## <30 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 1.7 0.0
## >30 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.6 0.0
## NO 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.2 0.0
##
## 684 685 686 691 692 693 694 695 696 698 7 70 701 702 703 704 705 706
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0
##
## 707 709 710 711 712 713 714 715 716 717 718 719 721 722 723 724 725 726
## <30 2.5 0.0 0.1 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.1 0.1 0.0 0.1 0.0 0.0
## >30 2.3 0.0 0.2 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.1 0.1 0.0 0.1 0.0 0.0
## NO 1.6 0.0 0.2 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.1 0.2 0.1 0.2 0.0 0.0
##
## 727 728 729 730 731 733 734 736 737 738 741 742 745 746 747 748 75 750
## <30 0.0 0.1 0.1 0.3 0.1 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.1 0.1 0.3 0.1 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.1 0.1 0.2 0.1 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 751 752 753 754 755 756 758 759 78 780 781 782 783 784 785 786 787 788
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.3 0.1 0.0 0.0 0.2 0.7 0.4 0.2 0.3
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.5 0.1 0.1 0.0 0.2 0.4 0.6 0.2 0.3
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.5 0.1 0.1 0.0 0.2 0.5 0.7 0.2 0.4
##
## 789 79 790 791 792 793 794 795 796 797 799 8 800 801 802 805 806 807
## <30 0.4 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.2 0.0 0.0 0.0 0.1 0.0 0.0
## >30 0.3 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.2 0.0 0.0 0.0 0.1 0.0 0.0
## NO 0.3 0.0 0.2 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.3 0.2 0.0 0.0 0.0 0.1 0.0 0.1
##
## 808 810 811 812 813 814 815 816 820 821 822 823 824 825 826 831 832 833
## <30 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 836 837 840 842 843 844 845 846 847 850 851 852 853 860 861 862 863 864
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 865 866 867 868 869 870 871 872 873 879 88 880 881 882 883 884 891 892
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 893 894 9 905 906 907 908 909 910 911 912 913 915 916 917 918 919 920
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 921 922 923 924 927 933 934 94 942 944 945 947 948 952 953 955 958 959
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 96 962 963 965 967 968 969 972 974 975 977 980 987 989 99 990 991 992
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 994 995 996 997 998 999 E812 E813 E814 E816 E817 E818 E819 E821 E826 E829
## <30 0.0 0.2 0.6 0.4 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.1 0.5 0.3 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.1 0.4 0.4 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## E849 E850 E853 E854 E858 E868 E870 E878 E879 E880 E881 E882 E883 E884
## <30 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0
##
## E885 E887 E888 E890 E900 E905 E906 E915 E916 E917 E918 E919 E924 E927
## <30 0.1 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## E928 E929 E930 E931 E932 E933 E934 E935 E936 E937 E938 E939 E941 E942
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## E944 E945 E947 E950 E965 E968 E980 V02 V03 V08 V09 V10 V11 V12 V13 V14
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.1 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.1 0.0 0.0
##
## V15 V16 V17 V18 V23 V25 V42 V43 V44 V45 V46 V49 V50 V53 V54 V55 V57 V58
## <30 0.1 0.0 0.0 0.0 0.0 0.0 0.3 0.1 0.0 0.4 0.0 0.1 0.0 0.0 0.1 0.0 0.0 0.2
## >30 0.1 0.0 0.0 0.0 0.0 0.0 0.3 0.1 0.0 0.4 0.0 0.1 0.0 0.0 0.1 0.0 0.0 0.2
## NO 0.1 0.0 0.0 0.0 0.0 0.0 0.3 0.1 0.0 0.4 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.1
##
## V60 V61 V62 V63 V64 V65 V66 V69 V70 V72 V85 V86
## <30 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0
## NO 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 4014.6, df = 1496, p-value < 2.2e-16
##
## [1] 9
## [1] "diag_3"
##
## ? 11 110 111 112 115 117 122 123 131 132 135 136 138
## <30 0.8 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1
## >30 1.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0
## NO 1.8 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0
##
## 139 14 141 146 148 150 151 152 153 154 155 156 157 158
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0
##
## 161 162 163 164 17 170 171 172 173 174 175 179 180 182
## <30 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0
## >30 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 183 185 186 188 189 191 192 193 195 196 197 198 199 200
## <30 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.6 0.2 0.1 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.3 0.1 0.0 0.0
## NO 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.4 0.3 0.0 0.0
##
## 201 202 203 204 205 208 211 214 215 216 217 218 220 223
## <30 0.0 0.1 0.0 0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.1 0.1 0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.1 0.1 0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.1 0.0 0.0
##
## 225 226 227 228 230 233 235 236 238 239 240 241 242 243
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0
##
## 244 245 246 250 250.01 250.02 250.03 250.1 250.11 250.12 250.13 250.2
## <30 0.4 0.0 0.0 8.7 1.0 1.6 0.1 0.0 0.0 0.1 0.0 0.0
## >30 0.5 0.0 0.0 10.0 1.0 1.5 0.2 0.0 0.0 0.0 0.0 0.0
## NO 0.6 0.0 0.0 12.8 0.8 1.2 0.1 0.0 0.0 0.0 0.0 0.0
##
## 250.21 250.22 250.23 250.3 250.31 250.4 250.41 250.42 250.43 250.5 250.51
## <30 0.0 0.0 0.0 0.0 0.0 0.6 0.3 0.3 0.1 0.1 0.2
## >30 0.0 0.0 0.0 0.0 0.0 0.5 0.3 0.1 0.1 0.1 0.1
## NO 0.0 0.0 0.0 0.0 0.0 0.3 0.1 0.1 0.1 0.1 0.1
##
## 250.52 250.53 250.6 250.7 250.8 250.81 250.82 250.83 250.9 250.91 250.92
## <30 0.1 0.1 1.7 0.2 0.4 0.1 0.1 0.0 0.0 0.0 0.2
## >30 0.0 0.0 1.3 0.2 0.4 0.1 0.1 0.0 0.0 0.0 0.2
## NO 0.1 0.0 0.8 0.1 0.3 0.0 0.1 0.0 0.0 0.0 0.1
##
## 250.93 251 252 253 255 256 258 259 260 261 262 263 265 266
## <30 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0
##
## 268 27 270 271 272 273 274 275 276 277 278 279 280 281
## <30 0.0 0.0 0.0 0.0 1.2 0.0 0.0 0.1 5.3 0.0 0.4 0.0 0.4 0.0
## >30 0.0 0.0 0.0 0.0 1.6 0.0 0.0 0.1 5.0 0.0 0.6 0.0 0.5 0.0
## NO 0.0 0.0 0.0 0.0 2.3 0.0 0.1 0.1 5.1 0.0 0.8 0.0 0.3 0.0
##
## 282 283 284 285 286 287 288 289 290 291 292 293 294 295
## <30 0.0 0.0 0.2 1.1 0.2 0.3 0.1 0.0 0.0 0.0 0.1 0.1 0.4 0.3
## >30 0.0 0.0 0.2 1.2 0.1 0.4 0.1 0.0 0.1 0.0 0.1 0.1 0.3 0.3
## NO 0.0 0.0 0.1 1.2 0.1 0.4 0.1 0.0 0.1 0.0 0.1 0.1 0.3 0.3
##
## 296 297 298 299 3 300 301 303 304 305 306 307 308 309
## <30 0.2 0.0 0.1 0.0 0.0 0.3 0.1 0.2 0.3 0.7 0.0 0.0 0.0 0.1
## >30 0.2 0.0 0.0 0.0 0.0 0.3 0.1 0.2 0.2 0.8 0.0 0.0 0.0 0.0
## NO 0.2 0.0 0.0 0.0 0.0 0.2 0.1 0.2 0.1 1.0 0.0 0.0 0.0 0.1
##
## 310 311 312 313 314 315 317 318 319 323 327 331 332 333
## <30 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.1 0.0
## >30 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.1 0.0
## NO 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.2 0.1 0.0
##
## 334 335 336 337 338 34 340 341 342 343 344 345 346 347
## <30 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.3 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.2 0.0 0.1 0.0 0.0 0.0
##
## 348 349 35 350 351 353 354 355 356 357 358 359 360 361
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0
## >30 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.0
## NO 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.0
##
## 362 365 365.44 366 368 369 370 372 373 374 376 377 378 379
## <30 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 38 380 381 382 383 384 385 386 387 388 389 391 394 395
## <30 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 396 397 398 401 402 403 404 405 41 410 411 412 413 414
## <30 0.3 0.1 0.0 6.1 0.2 3.4 0.2 0.0 0.5 0.2 0.3 0.3 0.2 3.0
## >30 0.3 0.2 0.0 7.0 0.4 2.8 0.1 0.0 0.7 0.2 0.4 0.3 0.4 3.8
## NO 0.2 0.1 0.0 9.3 0.4 1.8 0.1 0.0 0.8 0.2 0.4 0.4 0.3 3.6
##
## 415 416 417 42 420 421 423 424 425 426 427 428 429 430
## <30 0.0 0.2 0.0 0.1 0.0 0.0 0.0 1.0 1.2 0.2 4.0 4.9 0.1 0.0
## >30 0.0 0.2 0.0 0.0 0.0 0.0 0.0 1.2 1.3 0.3 4.2 5.3 0.1 0.0
## NO 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.9 1.0 0.3 3.6 3.9 0.1 0.0
##
## 431 432 433 434 435 436 437 438 440 441 442 443 444 445
## <30 0.0 0.0 0.1 0.1 0.1 0.0 0.1 0.4 0.3 0.1 0.0 0.2 0.1 0.0
## >30 0.0 0.0 0.2 0.0 0.1 0.0 0.1 0.3 0.2 0.0 0.0 0.3 0.1 0.0
## NO 0.0 0.0 0.1 0.1 0.1 0.0 0.1 0.3 0.2 0.1 0.0 0.2 0.0 0.0
##
## 446 447 448 451 452 453 454 455 456 457 458 459 460 461
## <30 0.0 0.1 0.0 0.0 0.0 0.3 0.0 0.1 0.0 0.0 0.2 0.1 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.1 0.1 0.0 0.3 0.1 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.1 0.0 0.0 0.3 0.1 0.0 0.0
##
## 462 463 464 465 466 47 470 472 473 475 477 478 480 481
## <30 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.1 0.2 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0
##
## 482 483 484 485 486 487 49 490 491 492 493 494 495 496
## <30 0.1 0.0 0.0 0.0 0.7 0.0 0.0 0.0 0.6 0.1 0.7 0.0 0.0 3.1
## >30 0.1 0.0 0.0 0.0 0.6 0.0 0.0 0.1 0.7 0.1 0.8 0.0 0.0 2.8
## NO 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.1 0.5 0.2 0.6 0.0 0.0 2.3
##
## 5 500 501 506 507 508 510 511 512 514 515 516 517 518
## <30 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.6 0.0 0.0 0.1 0.0 0.0 0.9
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.2 0.0 0.1 0.8
## NO 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.4 0.0 0.0 0.1 0.0 0.0 0.9
##
## 519 521 522 523 524 525 527 528 529 53 530 531 532 533
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.1 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.0 0.0
##
## 534 535 536 537 538 54 540 542 543 550 552 553 555 556
## <30 0.0 0.3 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0
## >30 0.0 0.3 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0
## NO 0.0 0.2 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0
##
## 557 558 560 562 564 565 566 567 568 569 57 570 571 572
## <30 0.0 0.1 0.3 0.2 0.1 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.4 0.1
## >30 0.0 0.1 0.2 0.2 0.1 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.3 0.0
## NO 0.0 0.1 0.3 0.2 0.1 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.3 0.1
##
## 573 574 575 576 577 578 579 580 581 582 583 584 585 586
## <30 0.1 0.1 0.0 0.1 0.2 0.3 0.0 0.0 0.3 0.0 0.1 1.1 3.0 0.0
## >30 0.0 0.1 0.0 0.0 0.1 0.3 0.0 0.0 0.1 0.0 0.1 1.0 2.4 0.0
## NO 0.0 0.1 0.0 0.0 0.1 0.2 0.0 0.0 0.1 0.0 0.1 0.9 1.5 0.0
##
## 588 590 591 592 593 594 595 596 597 598 599 600 601 602
## <30 0.0 0.0 0.1 0.1 0.3 0.0 0.1 0.1 0.0 0.0 2.1 0.0 0.0 0.0
## >30 0.0 0.1 0.1 0.1 0.3 0.0 0.1 0.1 0.0 0.0 2.0 0.1 0.0 0.0
## NO 0.0 0.0 0.1 0.1 0.3 0.0 0.0 0.1 0.0 0.0 1.8 0.1 0.0 0.0
##
## 603 604 605 607 608 610 611 614 616 617 618 619 620 621
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 622 623 624 625 626 627 641 642 643 644 646 647 648 649
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0
## NO 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0
##
## 652 653 654 655 656 657 658 659 66 660 661 663 664 665
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 669 670 671 674 680 681 682 684 685 686 690 692 693 694
## <30 0.0 0.0 0.0 0.0 0.0 0.1 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.1 1.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.1 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 695 696 697 698 7 70 701 702 703 704 705 706 707 708
## <30 0.0 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0 0.0 1.8 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 1.5 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 1.1 0.0
##
## 709 710 711 712 713 714 715 716 717 718 719 720 721 722
## <30 0.0 0.1 0.0 0.0 0.0 0.1 0.2 0.1 0.0 0.0 0.1 0.0 0.0 0.1
## >30 0.0 0.1 0.0 0.0 0.0 0.1 0.2 0.0 0.0 0.0 0.1 0.0 0.1 0.1
## NO 0.0 0.1 0.0 0.0 0.0 0.1 0.2 0.1 0.0 0.0 0.1 0.0 0.1 0.1
##
## 723 724 725 726 727 728 729 730 731 732 733 734 735 736
## <30 0.1 0.1 0.0 0.0 0.0 0.1 0.2 0.2 0.1 0.0 0.3 0.0 0.0 0.0
## >30 0.0 0.1 0.0 0.0 0.0 0.1 0.2 0.2 0.2 0.0 0.2 0.0 0.0 0.0
## NO 0.0 0.2 0.0 0.0 0.0 0.1 0.1 0.2 0.1 0.0 0.2 0.0 0.0 0.0
##
## 737 738 741 742 744 745 746 747 75 750 751 752 753 754
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 755 756 757 758 759 78 780 781 782 783 784 785 786 787
## <30 0.0 0.0 0.0 0.0 0.0 0.0 1.3 0.2 0.1 0.1 0.3 0.6 0.5 0.3
## >30 0.0 0.0 0.0 0.0 0.0 0.0 1.3 0.1 0.1 0.0 0.2 0.3 0.6 0.4
## NO 0.0 0.0 0.0 0.0 0.0 0.0 1.3 0.1 0.1 0.1 0.2 0.4 0.6 0.3
##
## 788 789 79 790 791 792 793 794 795 796 797 799 8 800
## <30 0.3 0.4 0.0 0.2 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.3 0.3 0.0
## >30 0.4 0.3 0.0 0.2 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.3 0.2 0.0
## NO 0.3 0.3 0.0 0.2 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.3 0.2 0.0
##
## 801 802 805 807 808 810 811 812 813 814 815 816 820 821
## <30 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 822 823 824 825 826 831 834 836 837 838 840 841 842 844
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 845 847 848 850 851 852 853 854 860 861 862 863 864 865
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 866 867 868 870 871 872 873 875 876 877 879 88 880 881
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 882 883 884 890 891 892 893 9 905 906 907 908 909 910
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 911 912 913 915 916 917 918 919 920 921 922 923 924 928
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 930 933 934 935 94 942 943 944 945 948 951 952 953 955
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 956 958 959 962 965 966 967 969 970 971 972 980 987 989
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## 991 992 995 996 997 998 999 E812 E813 E815 E816 E817 E818 E819
## <30 0.0 0.0 0.3 0.4 0.3 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.3 0.3 0.2 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.3 0.2 0.3 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## E822 E825 E826 E828 E849 E850 E852 E853 E854 E855 E858 E861 E864 E865
## <30 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## E870 E876 E878 E879 E880 E881 E882 E883 E884 E885 E886 E887 E888 E892
## <30 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.0
## >30 0.0 0.0 0.2 0.1 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.0
## NO 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.0
##
## E894 E900 E901 E904 E905 E906 E912 E915 E916 E917 E919 E920 E922 E924
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## E927 E928 E929 E930 E931 E932 E933 E934 E935 E936 E937 E938 E939 E941
## <30 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## E942 E943 E944 E945 E946 E947 E949 E950 E955 E956 E965 E966 E980 E987
## <30 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## NO 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
##
## V01 V02 V03 V06 V07 V08 V09 V10 V11 V12 V13 V14 V15 V16
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.3 0.0 0.0 0.3 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.2 0.0 0.2 0.0 0.0 0.3 0.0
## NO 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.3 0.0 0.2 0.0 0.0 0.4 0.0
##
## V17 V18 V22 V23 V25 V27 V42 V43 V44 V45 V46 V49 V53 V54
## <30 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.2 0.0 1.2 0.1 0.1 0.0 0.0
## >30 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.2 0.0 1.6 0.1 0.1 0.0 0.1
## NO 0.0 0.0 0.0 0.0 0.0 0.1 0.2 0.2 0.0 1.3 0.0 0.0 0.0 0.1
##
## V55 V57 V58 V60 V61 V62 V63 V64 V65 V66 V70 V72 V85 V86
## <30 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.1 0.0
## >30 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0
## NO 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 3938.8, df = 1578, p-value < 2.2e-16
##
## [1] 10
## [1] "max_glu_serum"
##
## >200 >300 None Norm
## <30 1.6 1.6 94.2 2.6
## >30 1.5 1.5 94.6 2.5
## NO 1.4 1.0 95.0 2.6
##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 51.922, df = 6, p-value = 1.933e-09
##
## [1] 11
## [1] "A1Cresult"
##
## >7 >8 None Norm
## <30 3.4 7.1 85.2 4.2
## >30 3.7 8.2 83.7 4.5
## NO 3.9 8.2 82.6 5.3
##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 70.124, df = 6, p-value = 3.856e-13
##
## [1] 12
## [1] "metformin"
##
## Down No Steady Up
## <30 0.6 82.9 15.7 0.8
## >30 0.5 81.1 17.4 1.0
## NO 0.6 79.3 18.9 1.2
##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 104.84, df = 6, p-value < 2.2e-16
##
## [1] 13
## [1] "repaglinide"
##
## Down No Steady Up
## <30 0.0 98.2 1.6 0.2
## >30 0.1 98.2 1.7 0.1
## NO 0.0 98.7 1.1 0.1
##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 58.965, df = 6, p-value = 7.303e-11
##
## [1] 14
## [1] "nateglinide"
##
## Down No Steady Up
## <30 0.0 99.3 0.7 0.0
## >30 0.0 99.3 0.7 0.0
## NO 0.0 99.3 0.6 0.0## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 3.4237, df = 6, p-value = 0.7541
##
## [1] 15
## [1] "chlorpropamide"
##
## Down No Steady Up
## <30 0.0 100.0 0.0 0.0
## >30 0.0 99.9 0.1 0.0
## NO 0.0 99.9 0.1 0.0## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 8.9556, df = 6, p-value = 0.1761
##
## [1] 16
## [1] "glimepiride"
##
## Down No Steady Up
## <30 0.2 95.3 4.1 0.3
## >30 0.2 94.6 4.9 0.3
## NO 0.2 95.0 4.5 0.3
##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 16.654, df = 6, p-value = 0.01064
##
## [1] 17
## [1] "acetohexamide"
##
## No Steady
## <30 100 0
## >30 100 0
## NO 100 0## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 1.863, df = 2, p-value = 0.394
##
## [1] 18
## [1] "glipizide"
##
## Down No Steady Up
## <30 0.7 87.2 11.2 0.9
## >30 0.6 86.7 11.9 0.8
## NO 0.5 88.1 10.7 0.7
##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 54.256, df = 6, p-value = 6.551e-10
##
## [1] 19
## [1] "glyburide"
##
## Down No Steady Up
## <30 0.5 90.0 8.8 0.7
## >30 0.6 89.5 9.1 0.8
## NO 0.5 89.4 9.2 0.8
##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 9.9938, df = 6, p-value = 0.1249
##
## [1] 20
## [1] "tolbutamide"
##
## No Steady
## <30 100 0
## >30 100 0
## NO 100 0## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 1.635, df = 2, p-value = 0.4415
##
## [1] 21
## [1] "pioglitazone"
##
## Down No Steady Up
## <30 0.2 93.2 6.4 0.3
## >30 0.1 92.2 7.4 0.3
## NO 0.1 93.1 6.6 0.2
##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 29.936, df = 6, p-value = 4.043e-05
##
## [1] 22
## [1] "rosiglitazone"
##
## Down No Steady Up
## <30 0.0 94.1 5.6 0.2
## >30 0.1 93.2 6.6 0.1
## NO 0.1 94.0 5.7 0.2
##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 43.009, df = 6, p-value = 1.162e-07
##
## [1] 23
## [1] "acarbose"
##
## Down No Steady Up
## <30 0.0 99.8 0.2 0.0
## >30 0.0 99.6 0.4 0.0
## NO 0.0 99.8 0.2 0.0## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 35.684, df = 6, p-value = 3.176e-06
##
## [1] 24
## [1] "miglitol"
##
## Down No Steady Up
## <30 0.0 100.0 0.0 0.0
## >30 0.0 99.9 0.0 0.0
## NO 0.0 100.0 0.0 0.0## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 11.594, df = 6, p-value = 0.07166
##
## [1] 25
## [1] "troglitazone"
##
## No Steady
## <30 100 0
## >30 100 0
## NO 100 0## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 1.4357, df = 2, p-value = 0.4878
##
## [1] 26
## [1] "tolazamide"
##
## No Steady Up
## <30 100 0 0
## >30 100 0 0
## NO 100 0 0## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 5.0863, df = 4, p-value = 0.2786for (i in seq(29,36)){
print(i)
print((names(fact[i])))
print(round(prop.table(table(fact$readmitted,fact[,i]),1)*100,1))
print(chisq.test(fact$readmitted,fact[,i]))#Ho: Independence
}## [1] 29
## [1] "insulin"
##
## Down No Steady Up
## <30 15.0 41.9 30.2 12.9
## >30 13.4 44.9 29.5 12.3
## NO 10.5 48.6 30.9 10.0
##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 516.7, df = 6, p-value < 2.2e-16
##
## [1] 30
## [1] "glyburide.metformin"
##
## Down No Steady Up
## <30 0.0 99.3 0.7 0.0
## >30 0.0 99.3 0.7 0.0
## NO 0.0 99.3 0.7 0.0## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 8.5245, df = 6, p-value = 0.2021
##
## [1] 31
## [1] "glipizide.metformin"
##
## No Steady
## <30 100 0
## >30 100 0
## NO 100 0## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 2.048, df = 2, p-value = 0.3592
##
## [1] 32
## [1] "glimepiride.pioglitazone"
##
## No Steady
## <30 100 0
## >30 100 0
## NO 100 0## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 1.863, df = 2, p-value = 0.394
##
## [1] 33
## [1] "metformin.rosiglitazone"
##
## No Steady
## <30 100 0
## >30 100 0
## NO 100 0## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 1.7098, df = 2, p-value = 0.4253
##
## [1] 34
## [1] "metformin.pioglitazone"
##
## No Steady
## <30 100 0
## >30 100 0
## NO 100 0## Warning in chisq.test(fact$readmitted, fact[, i]): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 0.85489, df = 2, p-value = 0.6522
##
## [1] 35
## [1] "change"
##
## Ch No
## <30 48.9 51.1
## >30 48.6 51.4
## NO 44.1 55.9
##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 215.83, df = 2, p-value < 2.2e-16
##
## [1] 36
## [1] "diabetesMed"
##
## No Yes
## <30 19.8 80.2
## >30 20.3 79.7
## NO 25.4 74.6
##
## Pearson's Chi-squared test
##
## data: fact$readmitted and fact[, i]
## X-squared = 386.51, df = 2, p-value < 2.2e-16Del análisis anterior se logran identificar los índices de los factores que parecen no brindar información significatuva para estimar el típo de readmisión (14,15,17,19,20,24,25,26,27,28,30,31,32,33,34).
Variables Categóricas a Utilizar
A continuación, se listan las 22 variables categóricas a utilizar. Por otro lado,teniendo en cuenta que el tipo de readmisión principal a estimar es el de readmisión en menos de 30 días desde la fecha de referencia, pues es el más grave, se decide reducir el número de niveles del campo readmisión de 3 a 2, unificando el tipo de admisión “mayor a 30 días” y “Sin readmisión”
Se define 0 para la caracteristicas de interés “menor a 30 días” Se define 1 para el complemento.(“mayor a 30 días”)
fact$readmitted<-ifelse(fact$readmitted=="<30",0,1)
fact$readmitted<-as.factor(fact$readmitted)
fact_r<-fact[,c(1:13,16,18,21,22,23,29,35,36,37)]
#head(fact_r)
names(fact_r)## [1] "race" "gender"
## [3] "age" "admission_type_id"
## [5] "discharge_disposition_id" "admission_source_id"
## [7] "diag_1" "diag_2"
## [9] "diag_3" "max_glu_serum"
## [11] "A1Cresult" "metformin"
## [13] "repaglinide" "glimepiride"
## [15] "glipizide" "pioglitazone"
## [17] "rosiglitazone" "acarbose"
## [19] "insulin" "change"
## [21] "diabetesMed" "readmitted"fact_r$readmitted<-as.factor(fact_r$readmitted)Posteriormente se procede a ampliar el límite de memoria y la numerización n a n(Construcción de Dummies) de las variables categóricas.
#One hot Encoding
memory.limit(size = 19000)## [1] 19000dmy <- dummyVars(" ~ .", data = fact_r[,-22])
fact_d <- data.frame(predict(dmy, newdata = fact_r[,-22]))#Factores numerización n:n
fact_d<-fact_d[,-fact_d$discharge_disposition_id.11]
readmitted<-fact_r$readmitted
fact_dr<-cbind(fact_d,readmitted)
#dim(fact_d)
#dim(fact_dr)
#Vista minable----
vm<-cbind(num_s,fact_dr)# Vista Mináble con Factores con numericos standarizados
#dim(vm)
prop.table(table(fact$readmitted))# Hay un pequeño desbalance para <30##
## 0 1
## 0.1115992 0.8884008Entrenamiento y Validación
Se procede a construir los conjuntos de entrenamiento y validación. Teniendo en cuenta que existe un ligero desbalance entre los niveles de la variable objetivo, también se realiza undersampling.
#creo entrenamiento y validacion
unos<-subset(vm,vm$readmitted==1)#white
cero<-subset(vm,vm$readmitted==0)#red
sample <- sample.int(nrow(unos), round(.5*nrow(unos)))
unos.train <- unos[sample, ]
unos.test <- unos[-sample, ]
#porcentaje que debo obtener para balancear entrenamiento
value=nrow(unos.train)/nrow(unos)
#value
#sacar ese numero en entrenamiento, el resto en validacion
sample <- sample.int(nrow(cero), round(value*nrow(cero)))
cero.train <- cero[sample, ]
cero.test <- cero[-sample, ]
#dim(cero.train)
#dim(cero.test)
#creo entrenamiento y validacion
data.train<-rbind(cero.train,unos.train)
data.test<-rbind(cero.test,unos.test)XGBoost
Se procede a ajustar el modelo de clasificación XGBoost
##
## Attaching package: 'xgboost'## The following object is masked from 'package:dplyr':
##
## slice##
## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr':
##
## between, first, last## The following object is masked from 'package:purrr':
##
## transpose## Loading required package: ParamHelpers##
## Attaching package: 'mlr'## The following object is masked from 'package:caret':
##
## train## [1] train-error:0.109420+0.000449 test-error:0.113007+0.001917
## [2] train-error:0.109218+0.000437 test-error:0.112515+0.002022
## [3] train-error:0.109120+0.000202 test-error:0.112161+0.001918
## [4] train-error:0.108983+0.000427 test-error:0.111827+0.002199
## [5] train-error:0.109081+0.000418 test-error:0.111768+0.002180
## [6] train-error:0.109086+0.000443 test-error:0.111808+0.002295
## [7] train-error:0.109312+0.000229 test-error:0.111906+0.002255
## [8] train-error:0.109194+0.000298 test-error:0.112004+0.002007
## [9] train-error:0.109184+0.000322 test-error:0.112083+0.002068
## [10] train-error:0.109105+0.000340 test-error:0.112024+0.002130
## [11] train-error:0.109149+0.000378 test-error:0.112083+0.002129
## [12] train-error:0.108948+0.000355 test-error:0.111965+0.001986
## [13] train-error:0.108929+0.000391 test-error:0.112161+0.002101## [1] val-error:0.112314 train-error:0.109548
## [2] val-error:0.112177 train-error:0.108860
## [3] val-error:0.111941 train-error:0.108840
## [4] val-error:0.111823 train-error:0.109253
## [5] val-error:0.111941 train-error:0.109194
## [6] val-error:0.111921 train-error:0.109528
## [7] val-error:0.111666 train-error:0.109233
## [8] val-error:0.111548 train-error:0.109253
## [9] val-error:0.111607 train-error:0.109548
## [10] val-error:0.111803 train-error:0.109528
## [11] val-error:0.111803 train-error:0.109430
## [12] val-error:0.111666 train-error:0.109115
## [13] val-error:0.111705 train-error:0.109213## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 71 76
## 1 5608 45129
##
## Accuracy : 0.8883
## 95% CI : (0.8855, 0.891)
## No Information Rate : 0.8884
## P-Value [Acc > NIR] : 0.5316
##
## Kappa : 0.0188
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.012502
## Specificity : 0.998319
## Pos Pred Value : 0.482993
## Neg Pred Value : 0.889469
## Prevalence : 0.111607
## Detection Rate : 0.001395
## Detection Prevalence : 0.002889
## Balanced Accuracy : 0.505410
##
## 'Positive' Class : 0
## 
- Se observa que todas las variables numéricas asociadas a la frecuencia de atención y uso del sistema son las más importantes de cara a predecir una readmisión al hospital. De igual forma, el lugar en el que el paciente fue dado de alta después del tratamiento, resulta ser valioso para la predicción.
-Por otro lado, con mayor disponibilidad de tiempo para el análisis se sugeriría hacer tratamiento de outliers, reducción de dimensionalidad y data binning.
-También se sugeriría correr otras modelos de clasificación más simples como una regresión logística o ensambles tipo bagging para comparar su desempeño.