REDES NEURONALES
Antonio Díaz A00837259
2025-08-25
Teoría
Una Red Neuronal Artificial (ANN) modela la relación entre un conjunto de entradas y una salida, resolviendo un problema de aprendizaje.
Ejemplos prácticos de aplicación de Redes Neuronales son: * La recomendación de contenido de Netflix. * El feed de Instagram o TIKTOK. *Determinar el número o letra escrita a mano.
install.packages(“neuralnet”)
Instalar paquetes y llamar librerías
library(neuralnet)Alimentar con ejemplos
examen <- c(20, 10, 30, 20, 80, 30)
proyecto <- c(90, 20, 40, 50, 50, 80)
estatus <- c(1, 0, 0, 0, 0, 1)
df <- data.frame(examen, proyecto, estatus)
df## examen proyecto estatus
## 1 20 90 1
## 2 10 20 0
## 3 30 40 0
## 4 20 50 0
## 5 80 50 0
## 6 30 80 1Generar la Red Neuronal
red_neuronal <- neuralnet(estatus ~ ., data=df)
plot(red_neuronal, rep="best")
Predecir la Red Neuronal
# Ejemplo de predicción con nuevos datos
prueba_examen <- c(30, 40, 85)
prueba_proyecto <- c(85, 50, 40)
prueba <- data.frame(examen = prueba_examen, proyecto = prueba_proyecto)
# Si entrenaste con nnet:
probabilidad <- predict(red_neuronal, prueba, type = "raw")
# Si entrenaste con neuralnet (cambiar por compute):
# probabilidad <- neuralnet::compute(red_neuronal, prueba)$net.result
resultado <- ifelse(probabilidad > 0.5, 1, 0)
resultado## [,1]
## [1,] 1
## [2,] 0
## [3,] 0
Instalar paquetes y llamar librerías
library(neuralnet)
library(dplyr)
# Cargar dataset
cancer <- read.csv("/Users/antoniodiaz/Desktop/MODULO2/cancer_de_mama.csv")
# Ver estructura
str(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 0.12440Preparar los datos
# Convertir Diagnosis a binario (M=1, B=0)
cancer$diagnosis <- ifelse(cancer$diagnosis == "M", 1, 0)
# Normalizar todas las variables excepto diagnosis
cancer_norm <- cancer %>%
mutate(across(-diagnosis, ~ (.-min(.))/(max(.)-min(.))))
summary(cancer_norm)## diagnosis radius_mean texture_mean perimeter_mean
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.2233 1st Qu.:0.2185 1st Qu.:0.2168
## Median :0.0000 Median :0.3024 Median :0.3088 Median :0.2933
## Mean :0.3726 Mean :0.3382 Mean :0.3240 Mean :0.3329
## 3rd Qu.:1.0000 3rd Qu.:0.4164 3rd Qu.:0.4089 3rd Qu.:0.4168
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## area_mean smoothness_mean compactness_mean concavity_mean
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.1174 1st Qu.:0.3046 1st Qu.:0.1397 1st Qu.:0.06926
## Median :0.1729 Median :0.3904 Median :0.2247 Median :0.14419
## Mean :0.2169 Mean :0.3948 Mean :0.2606 Mean :0.20806
## 3rd Qu.:0.2711 3rd Qu.:0.4755 3rd Qu.:0.3405 3rd Qu.:0.30623
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.00000
## concave_points_mean symmetry_mean fractal_dimension_mean radius_se
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.1009 1st Qu.:0.2823 1st Qu.:0.1630 1st Qu.:0.04378
## Median :0.1665 Median :0.3697 Median :0.2439 Median :0.07702
## Mean :0.2431 Mean :0.3796 Mean :0.2704 Mean :0.10635
## 3rd Qu.:0.3678 3rd Qu.:0.4530 3rd Qu.:0.3404 3rd Qu.:0.13304
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.00000
## texture_se perimeter_se area_se smoothness_se
## Min. :0.0000 Min. :0.00000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.1047 1st Qu.:0.04000 1st Qu.:0.02064 1st Qu.:0.1175
## Median :0.1653 Median :0.07209 Median :0.03311 Median :0.1586
## Mean :0.1893 Mean :0.09938 Mean :0.06264 Mean :0.1811
## 3rd Qu.:0.2462 3rd Qu.:0.12251 3rd Qu.:0.07170 3rd Qu.:0.2187
## Max. :1.0000 Max. :1.00000 Max. :1.00000 Max. :1.0000
## compactness_se concavity_se concave_points_se symmetry_se
## Min. :0.00000 Min. :0.00000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.08132 1st Qu.:0.03811 1st Qu.:0.1447 1st Qu.:0.1024
## Median :0.13667 Median :0.06538 Median :0.2070 Median :0.1526
## Mean :0.17444 Mean :0.08054 Mean :0.2235 Mean :0.1781
## 3rd Qu.:0.22680 3rd Qu.:0.10619 3rd Qu.:0.2787 3rd Qu.:0.2195
## Max. :1.00000 Max. :1.00000 Max. :1.0000 Max. :1.0000
## fractal_dimension_se radius_worst texture_worst perimeter_worst
## Min. :0.00000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.04675 1st Qu.:0.1807 1st Qu.:0.2415 1st Qu.:0.1678
## Median :0.07919 Median :0.2504 Median :0.3569 Median :0.2353
## Mean :0.10019 Mean :0.2967 Mean :0.3640 Mean :0.2831
## 3rd Qu.:0.12656 3rd Qu.:0.3863 3rd Qu.:0.4717 3rd Qu.:0.3735
## Max. :1.00000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## area_worst smoothness_worst compactness_worst concavity_worst
## Min. :0.00000 Min. :0.0000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.08113 1st Qu.:0.3000 1st Qu.:0.1163 1st Qu.:0.09145
## Median :0.12321 Median :0.3971 Median :0.1791 Median :0.18107
## Mean :0.17091 Mean :0.4041 Mean :0.2202 Mean :0.21740
## 3rd Qu.:0.22090 3rd Qu.:0.4942 3rd Qu.:0.3025 3rd Qu.:0.30583
## Max. :1.00000 Max. :1.0000 Max. :1.0000 Max. :1.00000
## concave_points_worst symmetry_worst fractal_dimension_worst
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.2231 1st Qu.:0.1851 1st Qu.:0.1077
## Median :0.3434 Median :0.2478 Median :0.1640
## Mean :0.3938 Mean :0.2633 Mean :0.1896
## 3rd Qu.:0.5546 3rd Qu.:0.3182 3rd Qu.:0.2429
## Max. :1.0000 Max. :1.0000 Max. :1.0000Dividir en train/test
set.seed(123)
indices <- sample(1:nrow(cancer_norm), size = 0.7 * nrow(cancer_norm))
train <- cancer_norm[indices, ]
test <- cancer_norm[-indices, ]
cat("Filas de train:", nrow(train), "\n")## Filas de train: 398cat("Filas de test:", nrow(test), "\n")## Filas de test: 171Entrar a la red neuronal
f <- as.formula(paste("diagnosis ~", paste(names(train)[!names(train) %in% "diagnosis"], collapse=" + ")))
red_cancer <- neuralnet(f, data=train, hidden=c(5,3), linear.output=FALSE)
plot(red_cancer)Importancia de las variables
library(NeuralNetTools)
library(ggplot2)
# Obtener importancia de variables
imp <- olden(red_cancer, bar_plot = FALSE)
# Pasar a data.frame
imp_df <- data.frame(Variable = rownames(imp), Importancia = imp[,1])
# Ordenar top 10
top_imp <- imp_df[order(abs(imp_df$Importancia), decreasing = TRUE), ][1:10, ]
# Graficar top 10
ggplot(top_imp, aes(x=reorder(Variable, Importancia), y=Importancia, fill=Importancia)) +
geom_bar(stat="identity") +
coord_flip() +
labs(title="Top 10 variables más importantes",
x="Variable", y="Importancia") +
theme_minimal()
Conclusión
El modelo mostró que las variables más importantes para predecir el cáncer de mama son area_se y radius_se, seguidas por características como concave_points y texture_worst. Esto confirma que tanto el tamaño como la irregularidad del tumor son factores clave en la clasificación. En conjunto, la red neuronal logra capturar patrones relevantes, reforzando su utilidad como apoyo al diagnóstico.
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YWJsZXMgbcOhcyBpbXBvcnRhbnRlcyBwYXJhIHByZWRlY2lyIGVsIGPDoW5jZXIgZGUgbWFtYSBzb24gYXJlYV9zZSB5IHJhZGl1c19zZSwgc2VndWlkYXMgcG9yIGNhcmFjdGVyw61zdGljYXMgY29tbyBjb25jYXZlX3BvaW50cyB5IHRleHR1cmVfd29yc3QuIEVzdG8gY29uZmlybWEgcXVlIHRhbnRvIGVsIHRhbWHDsW8gY29tbyBsYSBpcnJlZ3VsYXJpZGFkIGRlbCB0dW1vciBzb24gZmFjdG9yZXMgY2xhdmUgZW4gbGEgY2xhc2lmaWNhY2nDs24uIEVuIGNvbmp1bnRvLCBsYSByZWQgbmV1cm9uYWwgbG9ncmEgY2FwdHVyYXIgcGF0cm9uZXMgcmVsZXZhbnRlcywgcmVmb3J6YW5kbyBzdSB1dGlsaWRhZCBjb21vIGFwb3lvIGFsIGRpYWduw7NzdGljby4K