Cáncer de Mama
Mariel Garza A01285175
2025-08-21
Árboles de Decisión
Instalar paquetes y llamar librerías
# install.packages("rpart")
library(rpart)
# install.packages("rpart.plot")
library(rpart.plot)
# install.packages("neuralnet")
library(neuralnet)
# install.packages("caret")
library(caret)## Loading required package: ggplot2## Loading required package: latticeImportar la base de datos
cancer <- read.csv("/Users/marielgarza/Downloads/cancer_de_mama.csv")Entender la base de datos
summary(cancer)## 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. :0.20750str(cancer)## '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(cancer)## 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 0.12440Crear árbol de decisión
cancer$diagnosis <- as.factor(cancer$diagnosis)
str(cancer)## 'data.frame': 569 obs. of 31 variables:
## $ diagnosis : Factor w/ 2 levels "B","M": 2 2 2 2 2 2 2 2 2 2 ...
## $ 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 ...arbol_cancer <- rpart(diagnosis~., data=cancer)
rpart.plot(arbol_cancer)
prp(arbol_cancer, extra=7, prefix="fraccion\n")
Conclusiones
En conclusión, existen tres características clave para distinguir entre tumores benignos y malignos: el radio del núcleo, la concavidad y la textura. Los resultados muestran que los tumores con núcleos grandes y con bordes más irregulares tienden a ser malignos, mientras que los de menor tamaño y con menor concavidad se asocian a casos benignos.
Redes Neuronales
Importar la base de datos: cancer_de_mama
df1 <- read.csv("/Users/marielgarza/Downloads/cancer_de_mama.csv")
df1 <- data.frame(df1)
df1$diagnosis <- ifelse(df1$diagnosis == "M", 1, 0)
df1_scaled <- as.data.frame(scale(df1[ , !names(df1) %in% "diagnosis"]))
df1_scaled$diagnosis <- df1$diagnosisGenerar la Red Neuronal
red_neuronal <- neuralnet(df1_scaled$diagnosis ~ ., data=df1_scaled,
hidden=c(10,5), linear.output=FALSE)
plot(red_neuronal, rep="best")
Dividir de los datos
renglones_entrenamiento <- createDataPartition(df1_scaled$diagnosis, p=0.8, list=FALSE)
entrenamiento <- df1_scaled[renglones_entrenamiento, ]
prueba <- df1_scaled[-renglones_entrenamiento, ]Predecir la Red Neuronal
prediccion <- compute(red_neuronal, prueba[,-1])
probabilidad <- prediccion$net.result
resultado <- ifelse(probabilidad > 0.4, 1, 0)
resultado## [,1]
## 8 1
## 10 1
## 13 1
## 14 0
## 16 1
## 23 1
## 41 0
## 45 1
## 46 1
## 49 0
## 64 0
## 65 1
## 76 1
## 103 1
## 105 0
## 111 0
## 126 0
## 133 1
## 134 0
## 139 1
## 142 1
## 144 0
## 146 0
## 147 1
## 149 0
## 162 0
## 165 1
## 167 0
## 168 1
## 173 1
## 178 1
## 183 1
## 193 0
## 209 1
## 220 1
## 224 1
## 226 0
## 229 0
## 232 0
## 239 1
## 243 1
## 245 1
## 247 0
## 249 1
## 251 1
## 256 1
## 258 1
## 268 0
## 270 0
## 287 0
## 295 0
## 300 0
## 306 0
## 312 0
## 313 0
## 330 1
## 334 0
## 341 0
## 346 1
## 351 0
## 353 1
## 355 0
## 357 1
## 358 0
## 359 0
## 373 1
## 378 0
## 384 0
## 385 0
## 386 1
## 389 0
## 390 1
## 392 1
## 397 1
## 409 1
## 411 1
## 415 1
## 417 1
## 420 0
## 425 1
## 431 1
## 432 0
## 439 0
## 441 1
## 442 1
## 449 0
## 450 1
## 454 0
## 460 0
## 465 0
## 466 0
## 474 1
## 482 0
## 483 0
## 484 0
## 486 0
## 487 0
## 489 0
## 492 0
## 499 1
## 512 0
## 513 1
## 515 0
## 519 1
## 524 1
## 528 0
## 537 1
## 539 0
## 542 1
## 546 0
## 558 1
## 559 0
## 565 1Matriz de Confusión
mcrp <- confusionMatrix(
as.factor(resultado),
as.factor(prueba$diagnosis)
)
mcrp## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 53 4
## 1 18 38
##
## Accuracy : 0.8053
## 95% CI : (0.7202, 0.8738)
## No Information Rate : 0.6283
## P-Value [Acc > NIR] : 3.631e-05
##
## Kappa : 0.6097
##
## Mcnemar's Test P-Value : 0.005578
##
## Sensitivity : 0.7465
## Specificity : 0.9048
## Pos Pred Value : 0.9298
## Neg Pred Value : 0.6786
## Prevalence : 0.6283
## Detection Rate : 0.4690
## Detection Prevalence : 0.5044
## Balanced Accuracy : 0.8256
##
## 'Positive' Class : 0
## 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