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Predict Diabetes Diagnosis
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Predict Diabetes Diagnosis
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#detach all loaded packages
invisible(lapply(paste0('package:', names(sessionInfo()$otherPkgs)), detach, character.only=TRUE, unload = F))
library(caret)
library(MASS)
library(dplyr)
library(randomForest)
library(kernlab)
library(corrplot)
library(GGally)
library(pROC)
diabetes0 = read.csv("https://raw.githubusercontent.com/melissaalejandro/Class-Projects/main/diabetes.csv")
# ====================================================
# Data Overview ----
# ====================================================
# Predictors include number of pregnancies, glucose, Diastolic blood pressure (mm HG), skin fold thickness on triceps (mm), 2 hour serum insulin
# level (mu U/ml), body mass index, diabetes pedigree function, and age. The Target variable is labeled as 'Outcome' where 0 means 'no' (does not
# have diabetes) & 1 means yes (does have diabetes). The data consists of all females, age 21 and older who are of Pima Indian heritage.
# Summary statistics
summary(diabetes0)
# check the number of 0's in each column
cbind(lapply(colSums(diabetes0[1:8] == 0), sum))
## Filtering the data
# The variables BMI, Insulin, SkinThickness, BloodPressure, and Glucose have minimum values of zero. It's important to note that there are certain
# medical conditions where diastolic blood pressure, insulin levels, and glucose levels can be at zero. However, cases where a patient has a 0 BMI or
# 0 mm of skin thickness are extremely unlikely. Therefore, any rows with zeroes in SkinThickness and BMI will be removed.
# keep only rows where bmi and skin thickness are >0
diabetes = diabetes0 %>%
filter(diabetes0$BMI > 0 & diabetes0$SkinThickness > 0)
summary(diabetes)
str(diabetes)
# ====================================================
# Data Exploration ----
# ====================================================
# Variable relationship
ggpairs(diabetes,
lower = list(continuous = wrap('smooth', color='red', alpha=0.7, size=0.8)),
diag = list(continuous = wrap("densityDiag", fill='deepskyblue', alpha=0.9)),
upper = list(continuous = wrap('cor', color='black'))) # moderate correlation with glucose (0.464)
# correlation plot
corrplot(cor(diabetes), method="square", type = 'full', order = 'hclust', tl.col="black", tl.cex = .35, tl.srt=45, addCoef.col = 'black',number.cex=0.6)
# Boxplot Outliers
boxplot(diabetes, col = "deepskyblue")
summary(diabetes)
# Histogram: distribution of data
par(mfrow = c(3, 3))
for (i in 1:9) {
hist(diabetes[ ,i], xlab = names(diabetes[i]), main = paste(names(diabetes[i]), "Histogram"), col="deepskyblue")
}
# ====================================================
# Build the Model ----
# ====================================================
## Change labels
##### Label 0 -> No and 1 -> Yes #####
diabetes$Outcome[diabetes$Outcome==0] = "No"
diabetes$Outcome[diabetes$Outcome==1] = "Yes"
diabetes$Outcome = as.factor(diabetes$Outcome)
# split data
set.seed(100)
inTrain <- createDataPartition(as.matrix(diabetes[,9]), p = .8, list=FALSE)
diab.train = diabetes[inTrain,]
diab.test = diabetes[-inTrain,]
# preprocess
diab.TrainPP <- preProcess(diab.train, method = c("BoxCox", "scale", "center","nzv","spatialSign"))
diab.TestPP <- preProcess(diab.test, method = c("BoxCox", "scale", "center","nzv","spatialSign"))
# transform the dataset using the parameters ('predict' applies the transformations)
diabTrainTrans <- predict(diab.TrainPP, diab.train)
diabTestTrans <- predict(diab.TestPP, diab.test)
# check dist after transformation
par(mfrow = c(3, 3))
for (i in 1:8) {
hist(diabetes[ ,i], xlab = names(diabetes[i]), main = paste(names(diabetes[i]), "Histogram"), col="deepskyblue")
}
#control the computational nuances of the train function
set.seed(100)
ctrl <- trainControl(method = "LGOCV",
summaryFunction = twoClassSummary,
classProbs = TRUE,
savePredictions = TRUE)
# ====================================================
# Test Results ----
# ====================================================
##############################
# Logistic Regression ----
##############################
set.seed(5)
logisticTune <- train(x = diabTrainTrans[,1:8],
y = diabTrainTrans$Outcome,
method = "glm",
metric = "ROC",
trControl = ctrl)
logisticTune
# variable importance
plot(varImp(logisticTune, scale =FALSE))
test_results <- data.frame(obs = diabTestTrans$Outcome,
logistic = predict(logisticTune, diabTestTrans))
##############################
# PLSDA Regression ----
##############################
set.seed(476)
plsdaTune <- train(x = diabTrainTrans[,1:8],
y = diabTrainTrans$Outcome,
method = "pls",
metric = "ROC",
tuneGrid = expand.grid(.ncomp = 1:5),
trControl = ctrl)
plsdaTune
test_results$plsda <- predict(plsdaTune, newdata=diab.test[,-9])
plot(plsdaTune)
plot(varImp(plsdaTune, scale =FALSE))
##############################
# SVM Radial ----
##############################
set.seed(476)
#library(kernlab)
sigmaRangeReduced <- sigest(as.matrix(diabTrainTrans[,1:8]))
svmRGridReduced <- expand.grid(.sigma = sigmaRangeReduced[1],
.C = 2^(seq(-4, 4)))
svmRTune <- train(x = as.matrix(diabTrainTrans[,1:8]),
y = diabTrainTrans$Outcome,
method = "svmRadial", #svmLinear #svmPoly
metric = "ROC",
tuneGrid = svmRGridReduced,
fit = FALSE,
trControl = ctrl)
svmRTune
test_results$svm <- predict(svmRTune, newdata=diabTestTrans[,-9])
plot(varImp(svmRTune, scale =FALSE))
##############################
# Random Forest ----
##############################
mtryGrid <- data.frame(mtry = 1:8) #since we only have 8 predictors
### Tune the model using cross-validation
set.seed(476)
rfTune <- train(x = as.matrix(diabTrainTrans[,1:8]),
y = diabTrainTrans$Outcome,
method = "rf",
metric = "ROC",
tuneGrid = mtryGrid,
ntree = 150,
importance = TRUE,
trControl = ctrl)
rfTune
test_results$rf <- predict(rfTune, newdata=diab.test)
plot(rfTune)
plot(varImp(rfTune, scale =FALSE))
# ====================================================
# Predict the test set based on four models ----
# ====================================================
#logistic
diabTestTrans$logistic <- predict(logisticTune,diabTestTrans, type = "prob")[,1]
#PLSDA
diabTestTrans$plsda <- predict(plsdaTune,diabTestTrans, type = "prob")[,1]
#SVM
diabTestTrans$SVMR <- predict(svmRTune, diabTestTrans[,1:8], type = "prob")[,1]
#Random Forest
diabTestTrans$RF <- predict(rfTune,diabTestTrans, type = "prob")[,1]
# ====================================================
# ROC curves ----
# ====================================================
#library(pROC)
#ROC for logistic model
par(mfrow=c(1,1))
logisticROC <- roc(diabTestTrans$Outcome, diabTestTrans$logistic)
plot(logisticROC, col=1, lty=1, lwd=2)
#ROC for PLSDA
plsdaROC <- roc(diabTestTrans$Outcome, diabTestTrans$plsda)
lines(plsdaROC, col=3, lty=3, lwd=2)
#ROC for SVM
SVMROC <- roc(diabTestTrans$Outcome, diabTestTrans$SVMR)
lines(SVMROC, col=8, lty=8, lwd=2)
#ROC for Random Forest
RFROC <- roc(diabTestTrans$Outcome, diabTestTrans$RF)
lines(RFROC, col=4, lty=4, lwd=2)
legend('bottomright', c('logistic','plsda','SVM', 'Random Forest'), col=1:6, lty=1:5,lwd=2)
# ====================================================
# Create the confusion matrix from the test set ----
# ====================================================
#Confusion matrix of logistic model
a=confusionMatrix(data = predict(logisticTune, diabTestTrans), reference = diabTestTrans$Outcome)
#Confusion matrix of partial least squares discriminant analysis
b=confusionMatrix(data = predict(plsdaTune, diabTestTrans), reference = diabTestTrans$Outcome)
#Confusion matrix of SVM
c=confusionMatrix(data = predict(svmRTune, diabTestTrans[,1:8]), reference = diabTestTrans$Outcome)
#Confusion matrix of Random Forest
d=confusionMatrix(data = predict(rfTune, diabTestTrans), reference = diabTestTrans$Outcome)
# put results of confusion matrix into a df
results2 <- cbind(data.frame(logistic = c(a$overall[1:2], a$byClass[1:2])),
data.frame(plsda = c(b$overall[1:2], b$byClass[1:2])),
data.frame(svmR = c(c$overall[1:2], c$byClass[1:2])),
data.frame(rf = c(d$overall[1:2], d$byClass[1:2])))
# transpose df
(results2=t(results2))
# Create dot plot
res1 = resamples(list(Logistic = logisticTune, PLSDA = plsdaTune,
SVM = svmRTune, RandomForest = rfTune ))
dotplot(res1)