Cancer de mama

Instalar paquetes y llamar librerias

#install.packages("rpart")
library(rpart)
#install.packages("rpart.plot")
library(rpart.plot)
#install.packages("neuralnet")
library(neuralnet)
#install.packages("ggplot2") # Gráficas
library(ggplot2)
#install.packages("lattice") # Crear gráficos
library(lattice)
#install.packages ("caret") # Algoritmos de aprendizaje automático
library (caret)
#install.packages ("datasets") # Usar bases de datos, en este caso Iris
library(datasets)
#install.packages ("DataExplorer") # Análisis Exploratorio
library (DataExplorer)
#install.packages("kernlab")
library(kernlab)

## 
## Attaching package: 'kernlab'

## The following object is masked from 'package:ggplot2':
## 
##     alpha

Importar la base de datos

cancer_de_mama <- read.csv("C:\\Concentracion LIT\\Modulo2\\cancer_de_mama.csv")

Importar la base de datos

df <- data.frame(cancer_de_mama)

Entender la base de datos

summary(df)

##   diagnosis          radius_mean      texture_mean   perimeter_mean  
##  Length:569         Min.   : 6.981   Min.   : 9.71   Min.   : 43.79  
##  Class :character   1st Qu.:11.700   1st Qu.:16.17   1st Qu.: 75.17  
##  Mode  :character   Median :13.370   Median :18.84   Median : 86.24  
##                     Mean   :14.127   Mean   :19.29   Mean   : 91.97  
##                     3rd Qu.:15.780   3rd Qu.:21.80   3rd Qu.:104.10  
##                     Max.   :28.110   Max.   :39.28   Max.   :188.50  
##    area_mean      smoothness_mean   compactness_mean  concavity_mean   
##  Min.   : 143.5   Min.   :0.05263   Min.   :0.01938   Min.   :0.00000  
##  1st Qu.: 420.3   1st Qu.:0.08637   1st Qu.:0.06492   1st Qu.:0.02956  
##  Median : 551.1   Median :0.09587   Median :0.09263   Median :0.06154  
##  Mean   : 654.9   Mean   :0.09636   Mean   :0.10434   Mean   :0.08880  
##  3rd Qu.: 782.7   3rd Qu.:0.10530   3rd Qu.:0.13040   3rd Qu.:0.13070  
##  Max.   :2501.0   Max.   :0.16340   Max.   :0.34540   Max.   :0.42680  
##  concave_points_mean symmetry_mean    fractal_dimension_mean   radius_se     
##  Min.   :0.00000     Min.   :0.1060   Min.   :0.04996        Min.   :0.1115  
##  1st Qu.:0.02031     1st Qu.:0.1619   1st Qu.:0.05770        1st Qu.:0.2324  
##  Median :0.03350     Median :0.1792   Median :0.06154        Median :0.3242  
##  Mean   :0.04892     Mean   :0.1812   Mean   :0.06280        Mean   :0.4052  
##  3rd Qu.:0.07400     3rd Qu.:0.1957   3rd Qu.:0.06612        3rd Qu.:0.4789  
##  Max.   :0.20120     Max.   :0.3040   Max.   :0.09744        Max.   :2.8730  
##    texture_se      perimeter_se       area_se        smoothness_se     
##  Min.   :0.3602   Min.   : 0.757   Min.   :  6.802   Min.   :0.001713  
##  1st Qu.:0.8339   1st Qu.: 1.606   1st Qu.: 17.850   1st Qu.:0.005169  
##  Median :1.1080   Median : 2.287   Median : 24.530   Median :0.006380  
##  Mean   :1.2169   Mean   : 2.866   Mean   : 40.337   Mean   :0.007041  
##  3rd Qu.:1.4740   3rd Qu.: 3.357   3rd Qu.: 45.190   3rd Qu.:0.008146  
##  Max.   :4.8850   Max.   :21.980   Max.   :542.200   Max.   :0.031130  
##  compactness_se      concavity_se     concave_points_se   symmetry_se      
##  Min.   :0.002252   Min.   :0.00000   Min.   :0.000000   Min.   :0.007882  
##  1st Qu.:0.013080   1st Qu.:0.01509   1st Qu.:0.007638   1st Qu.:0.015160  
##  Median :0.020450   Median :0.02589   Median :0.010930   Median :0.018730  
##  Mean   :0.025478   Mean   :0.03189   Mean   :0.011796   Mean   :0.020542  
##  3rd Qu.:0.032450   3rd Qu.:0.04205   3rd Qu.:0.014710   3rd Qu.:0.023480  
##  Max.   :0.135400   Max.   :0.39600   Max.   :0.052790   Max.   :0.078950  
##  fractal_dimension_se  radius_worst   texture_worst   perimeter_worst 
##  Min.   :0.0008948    Min.   : 7.93   Min.   :12.02   Min.   : 50.41  
##  1st Qu.:0.0022480    1st Qu.:13.01   1st Qu.:21.08   1st Qu.: 84.11  
##  Median :0.0031870    Median :14.97   Median :25.41   Median : 97.66  
##  Mean   :0.0037949    Mean   :16.27   Mean   :25.68   Mean   :107.26  
##  3rd Qu.:0.0045580    3rd Qu.:18.79   3rd Qu.:29.72   3rd Qu.:125.40  
##  Max.   :0.0298400    Max.   :36.04   Max.   :49.54   Max.   :251.20  
##    area_worst     smoothness_worst  compactness_worst concavity_worst 
##  Min.   : 185.2   Min.   :0.07117   Min.   :0.02729   Min.   :0.0000  
##  1st Qu.: 515.3   1st Qu.:0.11660   1st Qu.:0.14720   1st Qu.:0.1145  
##  Median : 686.5   Median :0.13130   Median :0.21190   Median :0.2267  
##  Mean   : 880.6   Mean   :0.13237   Mean   :0.25427   Mean   :0.2722  
##  3rd Qu.:1084.0   3rd Qu.:0.14600   3rd Qu.:0.33910   3rd Qu.:0.3829  
##  Max.   :4254.0   Max.   :0.22260   Max.   :1.05800   Max.   :1.2520  
##  concave_points_worst symmetry_worst   fractal_dimension_worst
##  Min.   :0.00000      Min.   :0.1565   Min.   :0.05504        
##  1st Qu.:0.06493      1st Qu.:0.2504   1st Qu.:0.07146        
##  Median :0.09993      Median :0.2822   Median :0.08004        
##  Mean   :0.11461      Mean   :0.2901   Mean   :0.08395        
##  3rd Qu.:0.16140      3rd Qu.:0.3179   3rd Qu.:0.09208        
##  Max.   :0.29100      Max.   :0.6638   Max.   :0.20750

str(df)

## 'data.frame':    569 obs. of  31 variables:
##  $ diagnosis              : chr  "M" "M" "M" "M" ...
##  $ radius_mean            : num  18 20.6 19.7 11.4 20.3 ...
##  $ texture_mean           : num  10.4 17.8 21.2 20.4 14.3 ...
##  $ perimeter_mean         : num  122.8 132.9 130 77.6 135.1 ...
##  $ area_mean              : num  1001 1326 1203 386 1297 ...
##  $ smoothness_mean        : num  0.1184 0.0847 0.1096 0.1425 0.1003 ...
##  $ compactness_mean       : num  0.2776 0.0786 0.1599 0.2839 0.1328 ...
##  $ concavity_mean         : num  0.3001 0.0869 0.1974 0.2414 0.198 ...
##  $ concave_points_mean    : num  0.1471 0.0702 0.1279 0.1052 0.1043 ...
##  $ symmetry_mean          : num  0.242 0.181 0.207 0.26 0.181 ...
##  $ fractal_dimension_mean : num  0.0787 0.0567 0.06 0.0974 0.0588 ...
##  $ radius_se              : num  1.095 0.543 0.746 0.496 0.757 ...
##  $ texture_se             : num  0.905 0.734 0.787 1.156 0.781 ...
##  $ perimeter_se           : num  8.59 3.4 4.58 3.44 5.44 ...
##  $ area_se                : num  153.4 74.1 94 27.2 94.4 ...
##  $ smoothness_se          : num  0.0064 0.00522 0.00615 0.00911 0.01149 ...
##  $ compactness_se         : num  0.049 0.0131 0.0401 0.0746 0.0246 ...
##  $ concavity_se           : num  0.0537 0.0186 0.0383 0.0566 0.0569 ...
##  $ concave_points_se      : num  0.0159 0.0134 0.0206 0.0187 0.0188 ...
##  $ symmetry_se            : num  0.03 0.0139 0.0225 0.0596 0.0176 ...
##  $ fractal_dimension_se   : num  0.00619 0.00353 0.00457 0.00921 0.00511 ...
##  $ radius_worst           : num  25.4 25 23.6 14.9 22.5 ...
##  $ texture_worst          : num  17.3 23.4 25.5 26.5 16.7 ...
##  $ perimeter_worst        : num  184.6 158.8 152.5 98.9 152.2 ...
##  $ area_worst             : num  2019 1956 1709 568 1575 ...
##  $ smoothness_worst       : num  0.162 0.124 0.144 0.21 0.137 ...
##  $ compactness_worst      : num  0.666 0.187 0.424 0.866 0.205 ...
##  $ concavity_worst        : num  0.712 0.242 0.45 0.687 0.4 ...
##  $ concave_points_worst   : num  0.265 0.186 0.243 0.258 0.163 ...
##  $ symmetry_worst         : num  0.46 0.275 0.361 0.664 0.236 ...
##  $ fractal_dimension_worst: num  0.1189 0.089 0.0876 0.173 0.0768 ...

head(df)

##   diagnosis radius_mean texture_mean perimeter_mean area_mean smoothness_mean
## 1         M       17.99        10.38         122.80    1001.0         0.11840
## 2         M       20.57        17.77         132.90    1326.0         0.08474
## 3         M       19.69        21.25         130.00    1203.0         0.10960
## 4         M       11.42        20.38          77.58     386.1         0.14250
## 5         M       20.29        14.34         135.10    1297.0         0.10030
## 6         M       12.45        15.70          82.57     477.1         0.12780
##   compactness_mean concavity_mean concave_points_mean symmetry_mean
## 1          0.27760         0.3001             0.14710        0.2419
## 2          0.07864         0.0869             0.07017        0.1812
## 3          0.15990         0.1974             0.12790        0.2069
## 4          0.28390         0.2414             0.10520        0.2597
## 5          0.13280         0.1980             0.10430        0.1809
## 6          0.17000         0.1578             0.08089        0.2087
##   fractal_dimension_mean radius_se texture_se perimeter_se area_se
## 1                0.07871    1.0950     0.9053        8.589  153.40
## 2                0.05667    0.5435     0.7339        3.398   74.08
## 3                0.05999    0.7456     0.7869        4.585   94.03
## 4                0.09744    0.4956     1.1560        3.445   27.23
## 5                0.05883    0.7572     0.7813        5.438   94.44
## 6                0.07613    0.3345     0.8902        2.217   27.19
##   smoothness_se compactness_se concavity_se concave_points_se symmetry_se
## 1      0.006399        0.04904      0.05373           0.01587     0.03003
## 2      0.005225        0.01308      0.01860           0.01340     0.01389
## 3      0.006150        0.04006      0.03832           0.02058     0.02250
## 4      0.009110        0.07458      0.05661           0.01867     0.05963
## 5      0.011490        0.02461      0.05688           0.01885     0.01756
## 6      0.007510        0.03345      0.03672           0.01137     0.02165
##   fractal_dimension_se radius_worst texture_worst perimeter_worst area_worst
## 1             0.006193        25.38         17.33          184.60     2019.0
## 2             0.003532        24.99         23.41          158.80     1956.0
## 3             0.004571        23.57         25.53          152.50     1709.0
## 4             0.009208        14.91         26.50           98.87      567.7
## 5             0.005115        22.54         16.67          152.20     1575.0
## 6             0.005082        15.47         23.75          103.40      741.6
##   smoothness_worst compactness_worst concavity_worst concave_points_worst
## 1           0.1622            0.6656          0.7119               0.2654
## 2           0.1238            0.1866          0.2416               0.1860
## 3           0.1444            0.4245          0.4504               0.2430
## 4           0.2098            0.8663          0.6869               0.2575
## 5           0.1374            0.2050          0.4000               0.1625
## 6           0.1791            0.5249          0.5355               0.1741
##   symmetry_worst fractal_dimension_worst
## 1         0.4601                 0.11890
## 2         0.2750                 0.08902
## 3         0.3613                 0.08758
## 4         0.6638                 0.17300
## 5         0.2364                 0.07678
## 6         0.3985                 0.12440

Crear arbol de decision

cancer_de_mama <- cancer_de_mama[,c("diagnosis", "radius_mean", "texture_mean", "perimeter_mean", "area_mean", "smoothness_mean", "compactness_mean", "concavity_mean",
"concave_points_mean", "symmetry_mean", "fractal_dimension_mean")]

cancer_de_mama$diagnosis <- factor(cancer_de_mama$diagnosis, levels = c("M", "B"))

str(cancer_de_mama)

## 'data.frame':    569 obs. of  11 variables:
##  $ diagnosis             : Factor w/ 2 levels "M","B": 1 1 1 1 1 1 1 1 1 1 ...
##  $ radius_mean           : num  18 20.6 19.7 11.4 20.3 ...
##  $ texture_mean          : num  10.4 17.8 21.2 20.4 14.3 ...
##  $ perimeter_mean        : num  122.8 132.9 130 77.6 135.1 ...
##  $ area_mean             : num  1001 1326 1203 386 1297 ...
##  $ smoothness_mean       : num  0.1184 0.0847 0.1096 0.1425 0.1003 ...
##  $ compactness_mean      : num  0.2776 0.0786 0.1599 0.2839 0.1328 ...
##  $ concavity_mean        : num  0.3001 0.0869 0.1974 0.2414 0.198 ...
##  $ concave_points_mean   : num  0.1471 0.0702 0.1279 0.1052 0.1043 ...
##  $ symmetry_mean         : num  0.242 0.181 0.207 0.26 0.181 ...
##  $ fractal_dimension_mean: num  0.0787 0.0567 0.06 0.0974 0.0588 ...

arbol_cancer_de_mama <- rpart(diagnosis~., data=cancer_de_mama)
rpart.plot(arbol_cancer_de_mama)

prp(arbol_cancer_de_mama, extra=7, prefix = "fraccion\n")

analisis de arbol

En conclusion, las mas altas probabilidades de que el diagnostico sea benigno son

• 97%: Si el concave es menor de 0.051 y el area_mean menor de 696.

• 94%: Si el concave es mayor de 0.051 y texture menor de 16, pero concave es menor que 0.079

Y por el contrario, las mas altas probabilidades de un diagnostico maligno:

• 39%: Si el concave es menor a 0.051 y area_mean es mayor a 696
• 13%: Si el concave es mayor a 0.051

Partir la base de datos

# Normalmente 80-20
set.seed(123)
renglones_entrenamiento <- createDataPartition(df$diagnosis, p=0.8, list = FALSE)
entrenamiento <- cancer_de_mama[renglones_entrenamiento, ]
prueba <- cancer_de_mama[-renglones_entrenamiento, ]

Modelo 5. Redes Neuronales

modelo5 <- train(diagnosis ~ ., data=entrenamiento,
                 method = "nnet", #Cambiar
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method="cv", number=10)
                 
                 )

## # weights:  13
## initial  value 262.852887 
## iter  10 value 70.609437
## iter  20 value 64.546421
## iter  30 value 57.317690
## iter  40 value 55.655902
## iter  50 value 55.269971
## iter  60 value 55.215698
## iter  70 value 55.013382
## iter  80 value 54.983822
## iter  90 value 54.958822
## iter 100 value 54.933881
## final  value 54.933881 
## stopped after 100 iterations
## # weights:  37
## initial  value 324.647267 
## iter  10 value 52.761411
## iter  20 value 33.594391
## iter  30 value 22.089999
## iter  40 value 20.162890
## iter  50 value 19.591472
## iter  60 value 19.302073
## iter  70 value 19.061345
## iter  80 value 18.976040
## iter  90 value 18.950420
## iter 100 value 18.922419
## final  value 18.922419 
## stopped after 100 iterations
## # weights:  61
## initial  value 243.343199 
## iter  10 value 59.953810
## iter  20 value 44.536631
## iter  30 value 30.803207
## iter  40 value 18.863191
## iter  50 value 5.543866
## iter  60 value 0.980608
## iter  70 value 0.100439
## iter  80 value 0.000890
## final  value 0.000077 
## converged
## # weights:  13
## initial  value 312.188154 
## iter  10 value 79.343767
## iter  20 value 70.152394
## iter  30 value 69.474631
## iter  40 value 69.466812
## final  value 69.466798 
## converged
## # weights:  37
## initial  value 300.039791 
## iter  10 value 90.496889
## iter  20 value 60.262214
## iter  30 value 57.348992
## iter  40 value 52.740342
## iter  50 value 52.244307
## iter  60 value 52.183777
## final  value 52.183747 
## converged
## # weights:  61
## initial  value 284.785204 
## iter  10 value 66.835685
## iter  20 value 54.204007
## iter  30 value 50.926420
## iter  40 value 50.027544
## iter  50 value 49.635243
## iter  60 value 49.553447
## iter  70 value 49.540061
## iter  80 value 49.538812
## final  value 49.538695 
## converged
## # weights:  13
## initial  value 386.596775 
## iter  10 value 74.121021
## iter  20 value 62.224714
## iter  30 value 56.862866
## iter  40 value 56.408644
## iter  50 value 55.349097
## iter  60 value 55.163680
## iter  70 value 55.134719
## iter  80 value 55.127520
## iter  90 value 55.107772
## iter 100 value 55.107032
## final  value 55.107032 
## stopped after 100 iterations
## # weights:  37
## initial  value 393.083357 
## iter  10 value 50.515083
## iter  20 value 39.080672
## iter  30 value 29.692059
## iter  40 value 17.629483
## iter  50 value 10.266854
## iter  60 value 8.730551
## iter  70 value 8.394069
## iter  80 value 8.234457
## iter  90 value 8.169380
## iter 100 value 8.045147
## final  value 8.045147 
## stopped after 100 iterations
## # weights:  61
## initial  value 334.817714 
## iter  10 value 56.884241
## iter  20 value 30.194704
## iter  30 value 20.806781
## iter  40 value 16.607687
## iter  50 value 15.691824
## iter  60 value 15.345333
## iter  70 value 13.519343
## iter  80 value 13.023630
## iter  90 value 12.903240
## iter 100 value 12.842043
## final  value 12.842043 
## stopped after 100 iterations
## # weights:  13
## initial  value 281.991800 
## iter  10 value 72.256778
## iter  20 value 65.803482
## iter  30 value 59.756163
## iter  40 value 56.143223
## iter  50 value 55.729681
## iter  60 value 55.624762
## iter  70 value 55.371144
## iter  80 value 55.306779
## iter  90 value 55.294936
## iter 100 value 55.275715
## final  value 55.275715 
## stopped after 100 iterations
## # weights:  37
## initial  value 317.244142 
## iter  10 value 67.542742
## iter  20 value 51.031041
## iter  30 value 38.445924
## iter  40 value 31.587340
## iter  50 value 30.392223
## iter  60 value 28.401034
## iter  70 value 27.472274
## iter  80 value 27.197832
## iter  90 value 26.806286
## iter 100 value 26.308570
## final  value 26.308570 
## stopped after 100 iterations
## # weights:  61
## initial  value 254.499449 
## iter  10 value 50.469899
## iter  20 value 25.858280
## iter  30 value 7.856915
## iter  40 value 4.579678
## iter  50 value 4.306305
## iter  60 value 4.231443
## iter  70 value 4.185178
## iter  80 value 4.182189
## iter  90 value 4.179678
## iter 100 value 4.177928
## final  value 4.177928 
## stopped after 100 iterations
## # weights:  13
## initial  value 335.561056 
## iter  10 value 78.685485
## iter  20 value 70.711885
## iter  30 value 70.626683
## final  value 70.626504 
## converged
## # weights:  37
## initial  value 325.791203 
## iter  10 value 77.747932
## iter  20 value 59.102900
## iter  30 value 53.708151
## iter  40 value 51.547316
## iter  50 value 51.200146
## iter  60 value 51.179173
## final  value 51.179149 
## converged
## # weights:  61
## initial  value 280.382405 
## iter  10 value 63.464864
## iter  20 value 54.734037
## iter  30 value 50.168200
## iter  40 value 49.732482
## iter  50 value 49.669416
## iter  60 value 49.653787
## iter  70 value 49.651188
## iter  80 value 49.651023
## iter  80 value 49.651023
## iter  80 value 49.651023
## final  value 49.651023 
## converged
## # weights:  13
## initial  value 257.325771 
## iter  10 value 67.250226
## iter  20 value 56.677948
## iter  30 value 55.843217
## iter  40 value 55.741174
## iter  50 value 55.487364
## iter  60 value 55.457596
## iter  70 value 55.441353
## iter  80 value 55.428590
## final  value 55.428533 
## converged
## # weights:  37
## initial  value 353.282837 
## iter  10 value 72.767668
## iter  20 value 44.790648
## iter  30 value 33.179037
## iter  40 value 23.529448
## iter  50 value 22.188364
## iter  60 value 22.055879
## iter  70 value 21.980368
## iter  80 value 21.909945
## iter  90 value 21.700167
## iter 100 value 21.425272
## final  value 21.425272 
## stopped after 100 iterations
## # weights:  61
## initial  value 285.535637 
## iter  10 value 66.159903
## iter  20 value 42.952836
## iter  30 value 30.486378
## iter  40 value 24.460770
## iter  50 value 23.570634
## iter  60 value 23.272962
## iter  70 value 23.129872
## iter  80 value 23.074071
## iter  90 value 23.030569
## iter 100 value 22.987638
## final  value 22.987638 
## stopped after 100 iterations
## # weights:  13
## initial  value 325.445418 
## iter  10 value 65.979516
## iter  20 value 60.943063
## iter  30 value 59.636435
## iter  40 value 59.521077
## iter  50 value 59.333361
## iter  60 value 59.196536
## iter  70 value 59.183078
## iter  80 value 59.161068
## iter  90 value 59.151265
## iter 100 value 59.132267
## final  value 59.132267 
## stopped after 100 iterations
## # weights:  37
## initial  value 293.191054 
## iter  10 value 60.631711
## iter  20 value 45.781689
## iter  30 value 35.197322
## iter  40 value 28.570978
## iter  50 value 28.147799
## iter  60 value 28.141280
## final  value 28.141266 
## converged
## # weights:  61
## initial  value 278.048147 
## iter  10 value 57.860776
## iter  20 value 42.680948
## iter  30 value 30.713136
## iter  40 value 22.978257
## iter  50 value 22.047022
## iter  60 value 20.959509
## iter  70 value 20.694155
## iter  80 value 19.722669
## iter  90 value 19.386615
## iter 100 value 19.150788
## final  value 19.150788 
## stopped after 100 iterations
## # weights:  13
## initial  value 344.581959 
## iter  10 value 82.335483
## iter  20 value 74.562969
## iter  30 value 73.965780
## iter  40 value 73.965673
## iter  40 value 73.965673
## iter  40 value 73.965673
## final  value 73.965673 
## converged
## # weights:  37
## initial  value 283.758324 
## iter  10 value 87.979068
## iter  20 value 67.733996
## iter  30 value 59.102222
## iter  40 value 57.197596
## iter  50 value 56.755584
## iter  60 value 56.701886
## iter  70 value 56.696469
## final  value 56.696468 
## converged
## # weights:  61
## initial  value 287.735163 
## iter  10 value 120.134053
## iter  20 value 70.785500
## iter  30 value 60.360851
## iter  40 value 56.331088
## iter  50 value 54.340652
## iter  60 value 52.866634
## iter  70 value 52.614782
## iter  80 value 51.921005
## iter  90 value 51.806995
## iter 100 value 51.794007
## final  value 51.794007 
## stopped after 100 iterations
## # weights:  13
## initial  value 276.218345 
## iter  10 value 67.907362
## iter  20 value 61.951243
## iter  30 value 60.243385
## iter  40 value 59.844701
## iter  50 value 59.382291
## iter  60 value 59.308471
## iter  70 value 59.298074
## iter  80 value 59.286116
## iter  90 value 59.282725
## iter 100 value 59.280507
## final  value 59.280507 
## stopped after 100 iterations
## # weights:  37
## initial  value 316.372830 
## iter  10 value 73.083183
## iter  20 value 56.038105
## iter  30 value 51.043301
## iter  40 value 46.699658
## iter  50 value 43.542947
## iter  60 value 42.824038
## iter  70 value 42.737881
## iter  80 value 42.701566
## iter  90 value 42.632971
## iter 100 value 42.524928
## final  value 42.524928 
## stopped after 100 iterations
## # weights:  61
## initial  value 269.447214 
## iter  10 value 63.176539
## iter  20 value 45.472073
## iter  30 value 29.152276
## iter  40 value 21.533666
## iter  50 value 16.031765
## iter  60 value 13.460169
## iter  70 value 10.865326
## iter  80 value 10.247244
## iter  90 value 8.828201
## iter 100 value 7.380834
## final  value 7.380834 
## stopped after 100 iterations
## # weights:  13
## initial  value 308.075473 
## iter  10 value 106.333964
## iter  20 value 71.137861
## iter  30 value 68.815383
## iter  40 value 66.941865
## iter  50 value 62.652882
## iter  60 value 59.265303
## iter  70 value 57.131233
## iter  80 value 56.571066
## iter  90 value 56.544616
## iter 100 value 56.480040
## final  value 56.480040 
## stopped after 100 iterations
## # weights:  37
## initial  value 283.099446 
## iter  10 value 55.356200
## iter  20 value 41.853683
## iter  30 value 35.843577
## iter  40 value 33.083272
## iter  50 value 30.613026
## iter  60 value 29.229265
## iter  70 value 29.221361
## final  value 29.221341 
## converged
## # weights:  61
## initial  value 236.787035 
## iter  10 value 56.062201
## iter  20 value 29.960223
## iter  30 value 17.516557
## iter  40 value 14.942721
## iter  50 value 12.773162
## iter  60 value 12.078206
## iter  70 value 11.652492
## iter  80 value 11.611263
## iter  90 value 11.536497
## iter 100 value 11.510465
## final  value 11.510465 
## stopped after 100 iterations
## # weights:  13
## initial  value 277.722211 
## iter  10 value 82.357488
## iter  20 value 71.620764
## iter  30 value 71.562866
## final  value 71.562854 
## converged
## # weights:  37
## initial  value 320.661150 
## iter  10 value 72.379391
## iter  20 value 62.386250
## iter  30 value 59.927937
## iter  40 value 58.694006
## iter  50 value 56.522361
## iter  60 value 56.191582
## iter  70 value 56.044976
## iter  80 value 56.014059
## iter  90 value 56.010159
## iter 100 value 56.009932
## final  value 56.009932 
## stopped after 100 iterations
## # weights:  61
## initial  value 268.363518 
## iter  10 value 60.560796
## iter  20 value 53.023638
## iter  30 value 51.430024
## iter  40 value 51.245447
## iter  50 value 51.229162
## iter  60 value 51.226079
## final  value 51.226032 
## converged
## # weights:  13
## initial  value 316.003201 
## iter  10 value 101.165413
## iter  20 value 67.879378
## iter  30 value 65.007813
## iter  40 value 62.048440
## iter  50 value 59.890172
## iter  60 value 57.049484
## iter  70 value 56.685972
## iter  80 value 56.654793
## iter  90 value 56.577927
## iter 100 value 56.536653
## final  value 56.536653 
## stopped after 100 iterations
## # weights:  37
## initial  value 305.368958 
## iter  10 value 61.892640
## iter  20 value 46.747392
## iter  30 value 42.833666
## iter  40 value 40.185986
## iter  50 value 37.892448
## iter  60 value 37.146749
## iter  70 value 36.150920
## iter  80 value 34.463194
## iter  90 value 33.893850
## iter 100 value 33.565970
## final  value 33.565970 
## stopped after 100 iterations
## # weights:  61
## initial  value 319.563256 
## iter  10 value 75.990153
## iter  20 value 53.182537
## iter  30 value 44.403675
## iter  40 value 40.756581
## iter  50 value 40.081065
## iter  60 value 39.613274
## iter  70 value 38.914914
## iter  80 value 37.758051
## iter  90 value 35.296847
## iter 100 value 33.292523
## final  value 33.292523 
## stopped after 100 iterations
## # weights:  13
## initial  value 325.381328 
## iter  10 value 95.475671
## iter  20 value 65.913058
## iter  30 value 61.115061
## iter  40 value 60.516392
## iter  50 value 59.877605
## iter  60 value 59.590336
## iter  70 value 59.566637
## iter  80 value 59.546943
## iter  90 value 59.537225
## iter 100 value 59.523417
## final  value 59.523417 
## stopped after 100 iterations
## # weights:  37
## initial  value 307.480096 
## iter  10 value 71.615079
## iter  20 value 44.166916
## iter  30 value 36.683819
## iter  40 value 32.031082
## iter  50 value 28.433422
## iter  60 value 26.766362
## iter  70 value 25.721497
## iter  80 value 24.977988
## iter  90 value 24.495667
## iter 100 value 23.662677
## final  value 23.662677 
## stopped after 100 iterations
## # weights:  61
## initial  value 279.653579 
## iter  10 value 61.920271
## iter  20 value 34.150467
## iter  30 value 19.271675
## iter  40 value 14.542946
## iter  50 value 12.375336
## iter  60 value 11.820388
## iter  70 value 11.803922
## iter  80 value 11.803519
## iter  90 value 11.803461
## iter 100 value 11.797822
## final  value 11.797822 
## stopped after 100 iterations
## # weights:  13
## initial  value 270.876529 
## iter  10 value 95.463313
## iter  20 value 78.453604
## iter  30 value 75.257268
## iter  40 value 75.011359
## final  value 75.010000 
## converged
## # weights:  37
## initial  value 289.176647 
## iter  10 value 80.497270
## iter  20 value 70.646478
## iter  30 value 64.303828
## iter  40 value 59.137090
## iter  50 value 57.675692
## iter  60 value 56.024493
## iter  70 value 55.864407
## final  value 55.862473 
## converged
## # weights:  61
## initial  value 270.865458 
## iter  10 value 63.474512
## iter  20 value 56.759754
## iter  30 value 54.651040
## iter  40 value 54.324099
## iter  50 value 54.192998
## iter  60 value 54.170484
## final  value 54.170306 
## converged
## # weights:  13
## initial  value 375.460073 
## iter  10 value 91.934736
## iter  20 value 67.470354
## iter  30 value 62.328396
## iter  40 value 61.236009
## iter  50 value 59.925593
## iter  60 value 59.802929
## iter  70 value 59.749889
## iter  80 value 59.663648
## iter  90 value 59.660106
## iter 100 value 59.657428
## final  value 59.657428 
## stopped after 100 iterations
## # weights:  37
## initial  value 278.235329 
## iter  10 value 64.956310
## iter  20 value 57.879576
## iter  30 value 49.466890
## iter  40 value 45.147824
## iter  50 value 44.480322
## iter  60 value 44.377779
## iter  70 value 44.336233
## iter  80 value 44.275399
## iter  90 value 44.191846
## iter 100 value 43.969505
## final  value 43.969505 
## stopped after 100 iterations
## # weights:  61
## initial  value 305.301400 
## iter  10 value 58.938190
## iter  20 value 47.521714
## iter  30 value 38.260054
## iter  40 value 32.294146
## iter  50 value 30.837499
## iter  60 value 30.095982
## iter  70 value 29.865959
## iter  80 value 29.195772
## iter  90 value 28.867140
## iter 100 value 27.809151
## final  value 27.809151 
## stopped after 100 iterations
## # weights:  13
## initial  value 277.888455 
## iter  10 value 76.085314
## iter  20 value 70.892175
## iter  30 value 68.112778
## iter  40 value 61.137736
## iter  50 value 55.991875
## iter  60 value 54.794942
## iter  70 value 54.654357
## iter  80 value 54.556223
## iter  90 value 54.439895
## iter 100 value 54.436161
## final  value 54.436161 
## stopped after 100 iterations
## # weights:  37
## initial  value 269.070178 
## iter  10 value 77.112872
## iter  20 value 53.891203
## iter  30 value 48.616148
## iter  40 value 41.892497
## iter  50 value 40.000123
## iter  60 value 39.155672
## iter  70 value 39.056763
## iter  80 value 38.921617
## iter  90 value 38.911294
## iter 100 value 38.895349
## final  value 38.895349 
## stopped after 100 iterations
## # weights:  61
## initial  value 324.632672 
## iter  10 value 52.608073
## iter  20 value 34.962277
## iter  30 value 12.876760
## iter  40 value 6.922373
## iter  50 value 6.295801
## iter  60 value 6.042933
## iter  70 value 6.011247
## iter  80 value 6.004328
## iter  90 value 6.002370
## iter 100 value 6.000829
## final  value 6.000829 
## stopped after 100 iterations
## # weights:  13
## initial  value 271.400303 
## iter  10 value 88.802525
## iter  20 value 72.978182
## iter  30 value 69.153429
## iter  40 value 69.089103
## final  value 69.088902 
## converged
## # weights:  37
## initial  value 267.921620 
## iter  10 value 71.359843
## iter  20 value 57.042197
## iter  30 value 51.920283
## iter  40 value 50.802756
## iter  50 value 49.847178
## iter  60 value 49.813438
## final  value 49.813359 
## converged
## # weights:  61
## initial  value 258.039436 
## iter  10 value 62.789035
## iter  20 value 51.980621
## iter  30 value 49.497800
## iter  40 value 48.819106
## iter  50 value 48.730642
## iter  60 value 48.724063
## iter  70 value 48.723904
## final  value 48.723860 
## converged
## # weights:  13
## initial  value 310.013197 
## iter  10 value 67.558405
## iter  20 value 59.514670
## iter  30 value 57.648459
## iter  40 value 56.572218
## iter  50 value 55.624849
## iter  60 value 54.709826
## iter  70 value 54.559029
## iter  80 value 54.554354
## iter  90 value 54.512429
## iter 100 value 54.500709
## final  value 54.500709 
## stopped after 100 iterations
## # weights:  37
## initial  value 283.875978 
## iter  10 value 53.486716
## iter  20 value 34.315367
## iter  30 value 24.105189
## iter  40 value 21.507741
## iter  50 value 20.709017
## iter  60 value 20.060348
## iter  70 value 19.689740
## iter  80 value 18.699017
## iter  90 value 18.362967
## iter 100 value 18.189665
## final  value 18.189665 
## stopped after 100 iterations
## # weights:  61
## initial  value 379.954475 
## iter  10 value 48.437410
## iter  20 value 28.240148
## iter  30 value 11.711350
## iter  40 value 9.559550
## iter  50 value 8.777640
## iter  60 value 7.619139
## iter  70 value 7.347729
## iter  80 value 7.187601
## iter  90 value 6.777671
## iter 100 value 6.364409
## final  value 6.364409 
## stopped after 100 iterations
## # weights:  13
## initial  value 324.410517 
## iter  10 value 84.816249
## iter  20 value 62.394592
## iter  30 value 56.750953
## iter  40 value 55.703085
## iter  50 value 55.055702
## iter  60 value 54.961767
## iter  70 value 54.929265
## iter  80 value 54.838064
## iter  90 value 54.819884
## iter 100 value 54.812981
## final  value 54.812981 
## stopped after 100 iterations
## # weights:  37
## initial  value 269.221162 
## iter  10 value 70.434570
## iter  20 value 54.433343
## iter  30 value 51.506217
## iter  40 value 47.510624
## iter  50 value 44.782493
## iter  60 value 44.244064
## iter  70 value 43.670018
## iter  80 value 43.367356
## iter  90 value 42.949899
## iter 100 value 42.060650
## final  value 42.060650 
## stopped after 100 iterations
## # weights:  61
## initial  value 326.089993 
## iter  10 value 75.086289
## iter  20 value 50.203771
## iter  30 value 39.887849
## iter  40 value 19.131384
## iter  50 value 10.262409
## iter  60 value 6.523806
## iter  70 value 5.749826
## iter  80 value 5.612422
## iter  90 value 5.580629
## iter 100 value 5.426853
## final  value 5.426853 
## stopped after 100 iterations
## # weights:  13
## initial  value 283.068967 
## iter  10 value 85.552707
## iter  20 value 70.637593
## iter  30 value 68.614399
## final  value 68.613652 
## converged
## # weights:  37
## initial  value 254.824529 
## iter  10 value 65.658843
## iter  20 value 56.926527
## iter  30 value 54.490962
## iter  40 value 53.879918
## iter  50 value 52.803553
## iter  60 value 52.013978
## iter  70 value 51.978878
## final  value 51.978870 
## converged
## # weights:  61
## initial  value 260.300614 
## iter  10 value 60.449722
## iter  20 value 55.110815
## iter  30 value 52.791952
## iter  40 value 52.515255
## iter  50 value 52.205015
## iter  60 value 52.146965
## iter  70 value 52.143679
## iter  80 value 52.143610
## iter  80 value 52.143610
## iter  80 value 52.143610
## final  value 52.143610 
## converged
## # weights:  13
## initial  value 329.000231 
## iter  10 value 62.752072
## iter  20 value 56.483273
## iter  30 value 55.872167
## iter  40 value 55.474633
## iter  50 value 55.043434
## iter  60 value 55.023521
## iter  70 value 54.958635
## iter  80 value 54.937765
## iter  90 value 54.930371
## iter 100 value 54.927880
## final  value 54.927880 
## stopped after 100 iterations
## # weights:  37
## initial  value 261.912222 
## iter  10 value 55.808974
## iter  20 value 40.863949
## iter  30 value 34.101838
## iter  40 value 29.577958
## iter  50 value 22.084305
## iter  60 value 20.632028
## iter  70 value 20.513265
## iter  80 value 20.363023
## iter  90 value 20.239687
## iter 100 value 19.933996
## final  value 19.933996 
## stopped after 100 iterations
## # weights:  61
## initial  value 259.051940 
## iter  10 value 57.493600
## iter  20 value 42.490704
## iter  30 value 29.625857
## iter  40 value 26.916460
## iter  50 value 26.172427
## iter  60 value 25.685738
## iter  70 value 25.386348
## iter  80 value 25.234414
## iter  90 value 24.988845
## iter 100 value 24.751675
## final  value 24.751675 
## stopped after 100 iterations
## # weights:  13
## initial  value 278.071300 
## iter  10 value 67.850286
## iter  20 value 65.438084
## iter  30 value 62.017566
## iter  40 value 60.203221
## iter  50 value 50.030510
## iter  60 value 47.995987
## iter  70 value 46.939834
## iter  80 value 46.388661
## iter  90 value 46.185563
## iter 100 value 46.143183
## final  value 46.143183 
## stopped after 100 iterations
## # weights:  37
## initial  value 273.820200 
## iter  10 value 64.778819
## iter  20 value 48.472153
## iter  30 value 43.461353
## iter  40 value 39.578480
## iter  50 value 37.999421
## iter  60 value 37.276672
## iter  70 value 35.891676
## iter  80 value 34.326503
## iter  90 value 34.039900
## iter 100 value 33.122229
## final  value 33.122229 
## stopped after 100 iterations
## # weights:  61
## initial  value 283.840710 
## iter  10 value 51.914669
## iter  20 value 33.887810
## iter  30 value 19.546835
## iter  40 value 9.951235
## iter  50 value 6.217465
## iter  60 value 4.749076
## iter  70 value 4.608190
## iter  80 value 4.575615
## iter  90 value 4.558680
## iter 100 value 4.527658
## final  value 4.527658 
## stopped after 100 iterations
## # weights:  13
## initial  value 369.962017 
## iter  10 value 65.913748
## iter  20 value 63.294232
## iter  30 value 63.259302
## final  value 63.259243 
## converged
## # weights:  37
## initial  value 282.612857 
## iter  10 value 62.907576
## iter  20 value 57.827913
## iter  30 value 53.681578
## iter  40 value 52.614026
## iter  50 value 52.353180
## final  value 52.352922 
## converged
## # weights:  61
## initial  value 302.557884 
## iter  10 value 104.570918
## iter  20 value 58.065641
## iter  30 value 50.126336
## iter  40 value 48.251624
## iter  50 value 46.451957
## iter  60 value 45.089646
## iter  70 value 44.823981
## iter  80 value 44.750124
## iter  90 value 44.745345
## iter 100 value 44.741764
## final  value 44.741764 
## stopped after 100 iterations
## # weights:  13
## initial  value 325.081438 
## iter  10 value 77.631140
## iter  20 value 56.356405
## iter  30 value 47.622759
## iter  40 value 47.032439
## iter  50 value 46.298672
## iter  60 value 46.281553
## iter  70 value 46.267471
## iter  80 value 46.241995
## iter  90 value 46.230863
## iter 100 value 46.228926
## final  value 46.228926 
## stopped after 100 iterations
## # weights:  37
## initial  value 279.784132 
## iter  10 value 102.856216
## iter  20 value 47.618025
## iter  30 value 33.796173
## iter  40 value 26.598075
## iter  50 value 25.849298
## iter  60 value 23.422796
## iter  70 value 20.909278
## iter  80 value 20.447757
## iter  90 value 20.306948
## iter 100 value 20.193256
## final  value 20.193256 
## stopped after 100 iterations
## # weights:  61
## initial  value 313.482416 
## iter  10 value 48.653270
## iter  20 value 26.038472
## iter  30 value 18.417555
## iter  40 value 12.760284
## iter  50 value 12.206179
## iter  60 value 11.687000
## iter  70 value 11.381741
## iter  80 value 10.028461
## iter  90 value 8.386672
## iter 100 value 8.176887
## final  value 8.176887 
## stopped after 100 iterations
## # weights:  13
## initial  value 274.771174 
## iter  10 value 144.476155
## iter  20 value 72.664464
## iter  30 value 60.482983
## iter  40 value 59.686116
## iter  50 value 58.752324
## iter  60 value 58.464638
## iter  70 value 58.447498
## iter  80 value 58.428580
## iter  90 value 58.387254
## iter 100 value 58.377098
## final  value 58.377098 
## stopped after 100 iterations
## # weights:  37
## initial  value 324.074969 
## iter  10 value 77.168670
## iter  20 value 68.055599
## iter  30 value 59.176047
## iter  40 value 55.274191
## iter  50 value 53.952043
## iter  60 value 53.364337
## iter  70 value 52.798096
## iter  80 value 52.143576
## iter  90 value 51.779573
## iter 100 value 51.458241
## final  value 51.458241 
## stopped after 100 iterations
## # weights:  61
## initial  value 379.968368 
## iter  10 value 59.942786
## iter  20 value 37.158189
## iter  30 value 26.167883
## iter  40 value 21.399563
## iter  50 value 18.642912
## iter  60 value 18.078200
## iter  70 value 17.976713
## iter  80 value 17.793700
## iter  90 value 17.775135
## iter 100 value 17.771516
## final  value 17.771516 
## stopped after 100 iterations
## # weights:  13
## initial  value 326.658571 
## iter  10 value 84.873259
## iter  20 value 75.044847
## iter  30 value 72.932062
## iter  40 value 72.821204
## final  value 72.821192 
## converged
## # weights:  37
## initial  value 299.535328 
## iter  10 value 75.554356
## iter  20 value 64.467293
## iter  30 value 62.513598
## iter  40 value 59.770953
## iter  50 value 58.998230
## iter  60 value 55.990031
## iter  70 value 55.185493
## iter  80 value 55.147098
## iter  90 value 55.146293
## final  value 55.146282 
## converged
## # weights:  61
## initial  value 304.286848 
## iter  10 value 74.808195
## iter  20 value 58.251699
## iter  30 value 53.675946
## iter  40 value 52.613109
## iter  50 value 52.038174
## iter  60 value 51.668939
## iter  70 value 51.667307
## iter  80 value 51.667256
## final  value 51.667255 
## converged
## # weights:  13
## initial  value 268.635739 
## iter  10 value 75.137803
## iter  20 value 60.244611
## iter  30 value 59.204259
## iter  40 value 59.035366
## iter  50 value 58.623124
## iter  60 value 58.559731
## iter  70 value 58.544068
## iter  80 value 58.540926
## iter  90 value 58.539378
## iter 100 value 58.538769
## final  value 58.538769 
## stopped after 100 iterations
## # weights:  37
## initial  value 308.118402 
## iter  10 value 62.362742
## iter  20 value 36.839311
## iter  30 value 30.394619
## iter  40 value 22.554197
## iter  50 value 20.523708
## iter  60 value 19.791546
## iter  70 value 19.560044
## iter  80 value 19.394278
## iter  90 value 19.192373
## iter 100 value 19.006997
## final  value 19.006997 
## stopped after 100 iterations
## # weights:  61
## initial  value 254.777277 
## iter  10 value 64.208963
## iter  20 value 52.574731
## iter  30 value 45.992005
## iter  40 value 40.402501
## iter  50 value 36.202656
## iter  60 value 34.760116
## iter  70 value 33.936250
## iter  80 value 32.751202
## iter  90 value 31.785850
## iter 100 value 31.017513
## final  value 31.017513 
## stopped after 100 iterations
## # weights:  13
## initial  value 306.932897 
## iter  10 value 82.371728
## iter  20 value 73.734205
## iter  30 value 73.522758
## iter  40 value 73.301024
## iter  50 value 70.391470
## iter  60 value 68.357445
## iter  70 value 68.341943
## iter  80 value 68.327597
## iter  90 value 68.316353
## iter 100 value 68.313371
## final  value 68.313371 
## stopped after 100 iterations
## # weights:  37
## initial  value 335.698117 
## iter  10 value 61.288617
## iter  20 value 35.994641
## iter  30 value 30.061652
## iter  40 value 23.072574
## iter  50 value 20.402904
## iter  60 value 20.248272
## iter  70 value 20.208039
## iter  80 value 20.139913
## iter  90 value 20.127292
## iter 100 value 20.122751
## final  value 20.122751 
## stopped after 100 iterations
## # weights:  61
## initial  value 252.694737 
## iter  10 value 49.577846
## iter  20 value 31.320276
## iter  30 value 21.488279
## iter  40 value 12.493169
## iter  50 value 7.784448
## iter  60 value 2.857372
## iter  70 value 1.523516
## iter  80 value 1.418918
## iter  90 value 1.396565
## iter 100 value 1.393512
## final  value 1.393512 
## stopped after 100 iterations
## # weights:  13
## initial  value 349.360516 
## iter  10 value 75.405765
## iter  20 value 66.806545
## iter  30 value 65.863898
## iter  40 value 65.863414
## final  value 65.863408 
## converged
## # weights:  37
## initial  value 305.643248 
## iter  10 value 67.716761
## iter  20 value 58.326244
## iter  30 value 54.179827
## iter  40 value 52.699966
## iter  50 value 52.517284
## iter  60 value 52.515970
## final  value 52.515935 
## converged
## # weights:  61
## initial  value 325.036134 
## iter  10 value 86.835756
## iter  20 value 57.707410
## iter  30 value 52.723673
## iter  40 value 49.657244
## iter  50 value 48.780199
## iter  60 value 48.245937
## iter  70 value 48.040655
## iter  80 value 48.032839
## iter  90 value 48.032495
## final  value 48.032478 
## converged
## # weights:  13
## initial  value 330.458861 
## iter  10 value 72.291945
## iter  20 value 60.682734
## iter  30 value 59.183916
## iter  40 value 55.274108
## iter  50 value 53.018585
## iter  60 value 52.773363
## iter  70 value 52.687576
## iter  80 value 52.547734
## iter  90 value 52.539115
## iter 100 value 52.522081
## final  value 52.522081 
## stopped after 100 iterations
## # weights:  37
## initial  value 291.968487 
## iter  10 value 61.225814
## iter  20 value 46.006786
## iter  30 value 39.659708
## iter  40 value 36.771194
## iter  50 value 33.798337
## iter  60 value 32.894135
## iter  70 value 32.185694
## iter  80 value 30.014890
## iter  90 value 26.828129
## iter 100 value 26.501921
## final  value 26.501921 
## stopped after 100 iterations
## # weights:  61
## initial  value 331.010454 
## iter  10 value 55.745070
## iter  20 value 46.333696
## iter  30 value 31.831435
## iter  40 value 21.167175
## iter  50 value 13.052778
## iter  60 value 10.968315
## iter  70 value 10.048785
## iter  80 value 8.905155
## iter  90 value 7.769923
## iter 100 value 6.145303
## final  value 6.145303 
## stopped after 100 iterations
## # weights:  61
## initial  value 326.164850 
## iter  10 value 102.519277
## iter  20 value 77.587055
## iter  30 value 62.534833
## iter  40 value 59.525045
## iter  50 value 58.411250
## iter  60 value 57.897126
## iter  70 value 57.772405
## iter  80 value 57.760153
## iter  90 value 57.756674
## iter 100 value 57.755216
## final  value 57.755216 
## stopped after 100 iterations

resultado_entrenamiento5 <- predict(modelo5, entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)

#Matrices de confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasficiación.

#Matriz de confusion del resultado del entrenamiento
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$diagnosis)
mcre5

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   M   B
##          M 162   4
##          B   8 282
##                                           
##                Accuracy : 0.9737          
##                  95% CI : (0.9545, 0.9863)
##     No Information Rate : 0.6272          
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.9435          
##                                           
##  Mcnemar's Test P-Value : 0.3865          
##                                           
##             Sensitivity : 0.9529          
##             Specificity : 0.9860          
##          Pos Pred Value : 0.9759          
##          Neg Pred Value : 0.9724          
##              Prevalence : 0.3728          
##          Detection Rate : 0.3553          
##    Detection Prevalence : 0.3640          
##       Balanced Accuracy : 0.9695          
##                                           
##        'Positive' Class : M               
## 

# Matriz de confusion del resultado de la prueba
mcrp5 <- confusionMatrix(resultado_prueba5, prueba$diagnosis)
mcrp5

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  M  B
##          M 42  2
##          B  0 69
##                                           
##                Accuracy : 0.9823          
##                  95% CI : (0.9375, 0.9978)
##     No Information Rate : 0.6283          
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.9625          
##                                           
##  Mcnemar's Test P-Value : 0.4795          
##                                           
##             Sensitivity : 1.0000          
##             Specificity : 0.9718          
##          Pos Pred Value : 0.9545          
##          Neg Pred Value : 1.0000          
##              Prevalence : 0.3717          
##          Detection Rate : 0.3717          
##    Detection Prevalence : 0.3894          
##       Balanced Accuracy : 0.9859          
##                                           
##        'Positive' Class : M               
##