“Arbol de Decisión”

Cancer de Mama

#file.choose()
Cancer_de_mama <- read.csv ("/Users/ricardogc/Desktop/R - Analisis de datos para la toma de decisiones. /cancer_de_mama.csv")

Entender la base de datos

summary(Cancer_de_mama)

##  diagnosis  radius_mean      texture_mean   perimeter_mean     area_mean     
##  B:357     Min.   : 6.981   Min.   : 9.71   Min.   : 43.79   Min.   : 143.5  
##  M:212     1st Qu.:11.700   1st Qu.:16.17   1st Qu.: 75.17   1st Qu.: 420.3  
##            Median :13.370   Median :18.84   Median : 86.24   Median : 551.1  
##            Mean   :14.127   Mean   :19.29   Mean   : 91.97   Mean   : 654.9  
##            3rd Qu.:15.780   3rd Qu.:21.80   3rd Qu.:104.10   3rd Qu.: 782.7  
##            Max.   :28.110   Max.   :39.28   Max.   :188.50   Max.   :2501.0  
##  smoothness_mean   compactness_mean  concavity_mean    concave.points_mean
##  Min.   :0.05263   Min.   :0.01938   Min.   :0.00000   Min.   :0.00000    
##  1st Qu.:0.08637   1st Qu.:0.06492   1st Qu.:0.02956   1st Qu.:0.02031    
##  Median :0.09587   Median :0.09263   Median :0.06154   Median :0.03350    
##  Mean   :0.09636   Mean   :0.10434   Mean   :0.08880   Mean   :0.04892    
##  3rd Qu.:0.10530   3rd Qu.:0.13040   3rd Qu.:0.13070   3rd Qu.:0.07400    
##  Max.   :0.16340   Max.   :0.34540   Max.   :0.42680   Max.   :0.20120    
##  symmetry_mean    fractal_dimension_mean   radius_se        texture_se    
##  Min.   :0.1060   Min.   :0.04996        Min.   :0.1115   Min.   :0.3602  
##  1st Qu.:0.1619   1st Qu.:0.05770        1st Qu.:0.2324   1st Qu.:0.8339  
##  Median :0.1792   Median :0.06154        Median :0.3242   Median :1.1080  
##  Mean   :0.1812   Mean   :0.06280        Mean   :0.4052   Mean   :1.2169  
##  3rd Qu.:0.1957   3rd Qu.:0.06612        3rd Qu.:0.4789   3rd Qu.:1.4740  
##  Max.   :0.3040   Max.   :0.09744        Max.   :2.8730   Max.   :4.8850  
##   perimeter_se       area_se        smoothness_se      compactness_se    
##  Min.   : 0.757   Min.   :  6.802   Min.   :0.001713   Min.   :0.002252  
##  1st Qu.: 1.606   1st Qu.: 17.850   1st Qu.:0.005169   1st Qu.:0.013080  
##  Median : 2.287   Median : 24.530   Median :0.006380   Median :0.020450  
##  Mean   : 2.866   Mean   : 40.337   Mean   :0.007041   Mean   :0.025478  
##  3rd Qu.: 3.357   3rd Qu.: 45.190   3rd Qu.:0.008146   3rd Qu.:0.032450  
##  Max.   :21.980   Max.   :542.200   Max.   :0.031130   Max.   :0.135400  
##   concavity_se     concave.points_se   symmetry_se       fractal_dimension_se
##  Min.   :0.00000   Min.   :0.000000   Min.   :0.007882   Min.   :0.0008948   
##  1st Qu.:0.01509   1st Qu.:0.007638   1st Qu.:0.015160   1st Qu.:0.0022480   
##  Median :0.02589   Median :0.010930   Median :0.018730   Median :0.0031870   
##  Mean   :0.03189   Mean   :0.011796   Mean   :0.020542   Mean   :0.0037949   
##  3rd Qu.:0.04205   3rd Qu.:0.014710   3rd Qu.:0.023480   3rd Qu.:0.0045580   
##  Max.   :0.39600   Max.   :0.052790   Max.   :0.078950   Max.   :0.0298400   
##   radius_worst   texture_worst   perimeter_worst    area_worst    
##  Min.   : 7.93   Min.   :12.02   Min.   : 50.41   Min.   : 185.2  
##  1st Qu.:13.01   1st Qu.:21.08   1st Qu.: 84.11   1st Qu.: 515.3  
##  Median :14.97   Median :25.41   Median : 97.66   Median : 686.5  
##  Mean   :16.27   Mean   :25.68   Mean   :107.26   Mean   : 880.6  
##  3rd Qu.:18.79   3rd Qu.:29.72   3rd Qu.:125.40   3rd Qu.:1084.0  
##  Max.   :36.04   Max.   :49.54   Max.   :251.20   Max.   :4254.0  
##  smoothness_worst  compactness_worst concavity_worst  concave.points_worst
##  Min.   :0.07117   Min.   :0.02729   Min.   :0.0000   Min.   :0.00000     
##  1st Qu.:0.11660   1st Qu.:0.14720   1st Qu.:0.1145   1st Qu.:0.06493     
##  Median :0.13130   Median :0.21190   Median :0.2267   Median :0.09993     
##  Mean   :0.13237   Mean   :0.25427   Mean   :0.2722   Mean   :0.11461     
##  3rd Qu.:0.14600   3rd Qu.:0.33910   3rd Qu.:0.3829   3rd Qu.:0.16140     
##  Max.   :0.22260   Max.   :1.05800   Max.   :1.2520   Max.   :0.29100     
##  symmetry_worst   fractal_dimension_worst
##  Min.   :0.1565   Min.   :0.05504        
##  1st Qu.:0.2504   1st Qu.:0.07146        
##  Median :0.2822   Median :0.08004        
##  Mean   :0.2901   Mean   :0.08395        
##  3rd Qu.:0.3179   3rd Qu.:0.09208        
##  Max.   :0.6638   Max.   :0.20750

Crear arbol de decisión

library(rpart)
arbol <- rpart(formula=diagnosis ~ ., data = Cancer_de_mama )
arbol

## n= 569 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
##  1) root 569 212 B (0.62741652 0.37258348)  
##    2) radius_worst< 16.795 379  33 B (0.91292876 0.08707124)  
##      4) concave.points_worst< 0.1358 333   5 B (0.98498498 0.01501502) *
##      5) concave.points_worst>=0.1358 46  18 M (0.39130435 0.60869565)  
##       10) texture_worst< 25.67 19   4 B (0.78947368 0.21052632) *
##       11) texture_worst>=25.67 27   3 M (0.11111111 0.88888889) *
##    3) radius_worst>=16.795 190  11 M (0.05789474 0.94210526) *

library(rpart.plot)

rpart.plot(arbol)

??ggplot
#library("ggplot")
#library("tidyverse")
#ggplot(data=base_de_datos, mapping = aes (radius_worst, concave.points_worst))+geom_point(aes(color= diagnosis))+ theme_bw()

Conclusión

Obtuvimos una base de Datos de Cancer de Mama en el cual realizamos una grafica que nos brindo de forma visual los datos y así interpretarlos de una mejor manera. Pudimos obtener datos por medio de un Arbol de Decisión, obteniendo resultados enfocado a los tumores, como ejemplo su radius a 17 son benignos representando el 67% o mayor a 17 son Malignos. Y obtuvimos otra grafica que nos da información acorde a varables de Concave points wors y Radius Worst, brindandonos el angulo donde se encuentra el tumor y cual es su estado. Se tiene que tomar en cuenta que si no se cuenta con la versión actualizada, el arbol de decisión no se puede producir debido a la falta de paquetes que no pueden utilizarse en versiones anteriores.