| Variable | Level.of.Measurement |
|---|---|
| No.show | Nominal (Binary) |
| Age | Ratio |
| Gender | Nominal (Binary) |
| Alcoholism | Nominal (Binary) |
| Handcap | Nominal (Binary) |
| SMS_received | Nominal (Binary) |
| Diabetes | Nominal (Binary) |
| Hipertension | Nominal (Binary) |
BCE
Introduction
Missed appointments can delay medical care and make it harder for healthcare providers to use their time efficiently. By analyzing the relationship between multiple factors and appointment attendance, this project determines whether they are associated with a patient’s likelihood of missing an appointment.
Specifically, the factors we compared include age, alcoholism, gender, handicap, and SMS received. This information can be used to improve scheduling, reduce missed appointments, and make better use of available appointment times.
Variables and Level of Measurement
Qualitative and Quantitative
Using ScheduledDay, AppointmentDay, Neighbourhood as id variables
We imported the ‘Medical appointment no-shows’ data from Kaggle.
We split it into a qualitative and quantitive dataframes based on the variable type
Quantitiave Vars: Age, PatientId, AppointmentID, AppointmentDay, ScheduledDay
Qualtiative Vars: Gender, SMS_received, Scholarship, Hipertension, Diabetes, Alcoholism, Handcap, SMS_received, No.show
Gender SMS_received Scholarship Hipertension
Min. :0.00 Min. :0.000 Min. :0.00000 Min. :0.0000
1st Qu.:0.00 1st Qu.:0.000 1st Qu.:0.00000 1st Qu.:0.0000
Median :1.00 Median :0.000 Median :0.00000 Median :0.0000
Mean :0.65 Mean :0.321 Mean :0.09827 Mean :0.1972
3rd Qu.:1.00 3rd Qu.:1.000 3rd Qu.:0.00000 3rd Qu.:0.0000
Max. :1.00 Max. :1.000 Max. :1.00000 Max. :1.0000
Diabetes Alcoholism Handcap No.show
Min. :0.00000 Min. :0.0000 Min. :0.00000 Min. :0.0000
1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000
Median :0.00000 Median :0.0000 Median :0.00000 Median :0.0000
Mean :0.07186 Mean :0.0304 Mean :0.02028 Mean :0.2019
3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.0000
Max. :1.00000 Max. :1.0000 Max. :1.00000 Max. :1.0000 
Graphs
Covariance and Correlation
Age
[1] -0.5596021[1] -0.06031851Gender
[1] 0.0007886373[1] 0.004118633Alcoholism
[1] -1.351175e-05[1] -0.0001960437Handcap
[1] -0.0004119471[1] -0.007280746SMS_received
[1] 0.02369604[1] 0.1264307Residuals
Call:
lm(formula = No.show ~ Age, data = original)
Residuals:
Min 1Q Median 3Q Max
-0.2418 -0.2167 -0.1905 -0.1633 0.8797
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.408e-01 2.279e-03 105.65 <2e-16 ***
Age -1.048e-03 5.216e-05 -20.09 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4007 on 110525 degrees of freedom
Multiple R-squared: 0.003638, Adjusted R-squared: 0.003629
F-statistic: 403.6 on 1 and 110525 DF, p-value: < 2.2e-16 Min. 1st Qu. Median Mean 3rd Qu. Max.
-0.001048 -0.001048 -0.001048 -0.001048 -0.001048 -0.001048
Call:
lm(formula = No.show ~ Gender, data = original)
Residuals:
Min 1Q Median 3Q Max
-0.2031 -0.2031 -0.2031 -0.1997 0.8003
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.199679 0.002041 97.835 <2e-16 ***
Gender 0.003466 0.002532 1.369 0.171
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4014 on 110525 degrees of freedom
Multiple R-squared: 1.696e-05, Adjusted R-squared: 7.916e-06
F-statistic: 1.875 on 1 and 110525 DF, p-value: 0.1709 Min. 1st Qu. Median Mean 3rd Qu. Max.
0.003466 0.003466 0.003466 0.003466 0.003466 0.003466
Call:
lm(formula = No.show ~ Alcoholism, data = original)
Residuals:
Min 1Q Median 3Q Max
-0.2019 -0.2019 -0.2019 -0.2019 0.7985
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.2019465 0.0012263 164.680 <2e-16 ***
Alcoholism -0.0004584 0.0070333 -0.065 0.948
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4014 on 110525 degrees of freedom
Multiple R-squared: 3.843e-08, Adjusted R-squared: -9.009e-06
F-statistic: 0.004248 on 1 and 110525 DF, p-value: 0.948 Min. 1st Qu. Median Mean 3rd Qu. Max.
-0.0004584 -0.0004584 -0.0004584 -0.0004584 -0.0004584 -0.0004584
Call:
lm(formula = No.show ~ Handcap, data = original)
Residuals:
Min 1Q Median 3Q Max
-0.2024 -0.2024 -0.2024 -0.2024 0.8184
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.202353 0.001220 165.875 <2e-16 ***
Handcap -0.020738 0.008567 -2.421 0.0155 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4014 on 110525 degrees of freedom
Multiple R-squared: 5.301e-05, Adjusted R-squared: 4.396e-05
F-statistic: 5.859 on 1 and 110525 DF, p-value: 0.0155 Min. 1st Qu. Median Mean 3rd Qu. Max.
-0.02074 -0.02074 -0.02074 -0.02074 -0.02074 -0.02074
Call:
lm(formula = No.show ~ SMS_received, data = original)
Residuals:
Min 1Q Median 3Q Max
-0.2757 -0.1670 -0.1670 -0.1670 0.8330
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.167033 0.001454 114.90 <2e-16 ***
SMS_received 0.108712 0.002566 42.37 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3982 on 110525 degrees of freedom
Multiple R-squared: 0.01598, Adjusted R-squared: 0.01598
F-statistic: 1795 on 1 and 110525 DF, p-value: < 2.2e-16 Min. 1st Qu. Median Mean 3rd Qu. Max.
0.1087 0.1087 0.1087 0.1087 0.1087 0.1087 Summary Stats
Summary Statistics
===========================================
Statistic N Mean St. Dev. Min Max
-------------------------------------------
No.show 110,527 0.20 0.40 0 1
Age 110,527 37.09 23.11 -1 115
Gender 110,527 0.65 0.48 0 1
Alcoholism 110,527 0.03 0.17 0 1
Handcap 110,527 0.02 0.14 0 1
SMS_received 110,527 0.32 0.47 0 1
-------------------------------------------
Regression Results
==========================================================
Dependent variable:
---------------------------------------------
No.show
Model 1 Model 2 Model 3
(1) (2) (3) (4) (5)
----------------------------------------------------------
Age -0.001***
(0.0001)
Gender 0.003
(0.003)
Alcoholism -0.0005
(0.007)
Handicap -0.021**
(0.009)
SMS Received 0.109***
(0.003)
Constant 0.241*** 0.200*** 0.202*** 0.202*** 0.167***
(0.002) (0.002) (0.001) (0.001) (0.001)
----------------------------------------------------------
Observations 110,527 110,527 110,527 110,527 110,527
R2 0.004 0.00002 0.00000 0.0001 0.016
Adjusted R2 0.004 0.00001 -0.00001 0.00004 0.016
==========================================================
Note: *p<0.1; **p<0.05; ***p<0.01Equation
\[ \text{No.show}_i = \beta_0 + \beta_1\text{Age}_i + \beta_2\text{Gender}_i + \beta_3\text{Alcoholism}_i + \beta_4\text{Handcap}_i + \beta_5\text{SMS\_received}_i + \varepsilon_i \]