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model_selection.R
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library(randomForest)
library(caret)
library(doParallel)
library(PRROC)
setwd("C:/Users/mxi1/Dropbox/medical/2022")
##########################################################################
# descriptive statistics
##########################################################################
data <- readRDS('result/thyroid_clean.rds')
table(data$id)
length(unique(data$id))
# remove id
data <- data[,-1]
str(data)
summary(data)
median(data$age);IQR(data$age)
table(data$gender)/dim(data)[1]
median(data$FT3);IQR(data$FT3)
median(data$FT4);IQR(data$FT4)
median(data$TSH);IQR(data$TSH)
median(data$TPO);IQR(data$TPO)
median(data$TGAb);IQR(data$TGAb)
mean(data$FT3);sd(data$FT3)
mean(data$FT4);sd(data$FT4)
mean(data$TSH);sd(data$TSH)
mean(data$TPO);sd(data$TPO)
mean(data$TGAb);sd(data$TGAb)
table(data$site)/dim(data)[1]
table(data$echo_pattern)/dim(data)[1]
table(data$multifocality)/dim(data)[1]
mean(data$size);sd(data$size)
median(data$size);IQR(data$size)
table(data$shape)/dim(data)[1]
table(data$margin)/dim(data)[1]
table(data$calcification)/dim(data)[1]
table(data$echo_strength)/dim(data)[1]
table(data$blood_flow)/dim(data)[1]
table(data$composition)/dim(data)[1]
table(data$multilateral)/dim(data)[1]
table(data$mal)/dim(data)[1]
##########################################################################
# logistic regression
##########################################################################
system.time({
acc <- c()
auroc <- c()
sen <- c()
spec <- c()
prec <- c()
# repeat 10-fold cv by 10 times
for(seed in 1:10){
print(seed)
set.seed(seed)
data <- readRDS('result/thyroid_clean.rds')
index <- sample(unique(data$id))
# 10-fold cross-validation by patients
index.list <- split(index, cut(seq_along(index), 10, labels = FALSE))
result <- data.frame()
for(index.test in index.list){
#index.test <- index.list[[1]]
index.train <- setdiff(index, index.test)
test <- data[data$id%in%index.test,]
train <- data[data$id%in%index.train,]
model <- glm(mal~., family = 'binomial', data = train[,-1])
pred.prob <- predict(model, test[,-1], type = 'response')
result <- rbind(result, cbind(row.names(pred.prob), pred.prob))
}
data[row.names(result), 'pred.prob'] <- result$pred.prob
data$pred.mal <- as.factor(ifelse(data$pred.prob >= 0.5, 1, 0))
cm <- confusionMatrix(data$pred.mal, data$mal, mode = 'everything', positive = '1')
roc <- roc.curve(scores.class0 = data[data$mal=='1','pred.prob'],
scores.class1 = data[data$mal=='0','pred.prob'], curve=F); roc$auc
acc[seed] <- cm[["overall"]][["Accuracy"]]
auroc[seed] <- roc$auc
sen[seed] <- cm[["byClass"]][["Sensitivity"]]
spec[seed] <- cm[["byClass"]][["Specificity"]]
prec[seed] <- cm[["byClass"]][["Precision"]]
}
})
mean(acc); mean(auroc); mean(sen); mean(spec); mean(prec)
##########################################################################
# random forest
##########################################################################
system.time({
acc <- c()
auroc <- c()
sen <- c()
spec <- c()
prec <- c()
f1 <- c()
for(seed in 1:10){
print(seed)
set.seed(seed)
data <- readRDS('result/thyroid_clean.rds')
index <- sample(unique(data$id))
# 10-fold cross-validation by patients
index.list <- split(index, cut(seq_along(index), 10, labels = FALSE))
result <- data.frame()
for(index.test in index.list){
#index.test <- index.list[[10]]
index.train <- setdiff(index, index.test)
test <- data[data$id%in%index.test,]
train <- data[data$id%in%index.train,]
model <- randomForest(mal~., data=train[,-1])
pred.prob <- predict(model, test[,-1], type = 'prob')[,2]
result <- rbind(result, cbind(row.names(pred.prob), pred.prob))
}
data[row.names(result), 'pred.prob'] <- result$pred.prob
data$pred.mal <- as.factor(ifelse(data$pred.prob >= 0.5, 1, 0))
cm <- confusionMatrix(data$pred.mal, data$mal, mode = 'everything', positive = '1'); cm[["overall"]][["Accuracy"]]
roc <- roc.curve(scores.class0 = data[data$mal=='1','pred.prob'],
scores.class1 = data[data$mal=='0','pred.prob'], curve=F); roc$auc
acc[seed] <- cm[["overall"]][["Accuracy"]]
auroc[seed] <- roc$auc
sen[seed] <- cm[["byClass"]][["Sensitivity"]]
spec[seed] <- cm[["byClass"]][["Specificity"]]
prec[seed] <- cm[["byClass"]][["Precision"]]
f1[seed] <- cm[["byClass"]][["F1"]]
}
})
mean(acc); mean(auroc); mean(sen); mean(spec); mean(prec); mean(f1)
##########################################################################
# lda, qda, naive bayes
##########################################################################
library(MASS)
library(e1071)
system.time({
acc <- c()
auroc <- c()
sen <- c()
spec <- c()
prec <- c()
for(seed in 1:10){
print(seed)
set.seed(seed)
data <- readRDS('result/thyroid_clean.rds')
index <- sample(unique(data$id))
index.list <- split(index, cut(seq_along(index), 10, labels = FALSE))
result <- data.frame()
for(index.test in index.list){
#index.test <- index.list[[2]]
index.train <- setdiff(index, index.test)
test <- data[data$id%in%index.test,]
train <- data[data$id%in%index.train,]
# lda
model <- lda(mal~., data=train[,-1])
pred <- predict(model, test[,-1])
pred.prob <- pred[["posterior"]][,2]
# qda
#model <- qda(mal~., data=train)
#pred <- predict(model, test)
#pred.prob <- pred[["posterior"]][,2]
# naive bayes
#model <- naiveBayes(mal~., data=train)
#pred <- predict(model, test, type = 'raw')
#pred.prob <- pred[,2]
result <- rbind(result, cbind(row.names(pred.prob), pred.prob))
}
data[row.names(result), 'pred.prob'] <- result$pred.prob
data$pred.mal <- as.factor(ifelse(data$pred.prob >= 0.5, 1, 0))
cm <- confusionMatrix(data$pred.mal, data$mal, mode = 'everything', positive = '1'); cm[["overall"]][["Accuracy"]]
roc <- roc.curve(scores.class0 = data[data$mal=='1','pred.prob'],
scores.class1 = data[data$mal=='0','pred.prob'], curve=F); roc$auc
acc[seed] <- cm[["overall"]][["Accuracy"]]
auroc[seed] <- roc$auc
sen[seed] <- cm[["byClass"]][["Sensitivity"]]
spec[seed] <- cm[["byClass"]][["Specificity"]]
prec[seed] <- cm[["byClass"]][["Precision"]]
}
})
mean(acc); mean(auroc); mean(sen); mean(spec); mean(prec)
##########################################################################
# gbm
##########################################################################
library(gbm)
system.time({
acc <- c()
auroc <- c()
sen <- c()
spec <- c()
prec <- c()
for(seed in 1:10){
print(seed)
set.seed(seed)
data <- readRDS('result/thyroid_clean.rds')
#
data$mal <- as.character(data$mal)
#
index <- sample(unique(data$id))
index.list <- split(index, cut(seq_along(index), 10, labels = FALSE))
result <- data.frame()
for(index.test in index.list){
#index.test <- index.list[[1]]
index.train <- setdiff(index, index.test)
test <- data[data$id%in%index.test,]
train <- data[data$id%in%index.train,]
model <- gbm(mal~., data=train[,-1], verbose = F)
pred.prob <- predict(object = model, newdata = test[,-1], type = 'response')
result <- rbind(result, cbind(row.names(test), pred.prob))
}
data[result$V1, 'pred.prob'] <- result$pred.prob
#
data$mal <- as.factor(data$mal)
#
data$pred.mal <- as.factor(ifelse(data$pred.prob >= 0.5, 1, 0))
cm <- confusionMatrix(data$pred.mal, data$mal, mode = 'everything', positive = '1'); cm[["overall"]][["Accuracy"]]
roc <- roc.curve(scores.class0 = data[data$mal=='1','pred.prob'],
scores.class1 = data[data$mal=='0','pred.prob'], curve=F); roc$auc
acc[seed] <- cm[["overall"]][["Accuracy"]]
auroc[seed] <- roc$auc
sen[seed] <- cm[["byClass"]][["Sensitivity"]]
spec[seed] <- cm[["byClass"]][["Specificity"]]
prec[seed] <- cm[["byClass"]][["Precision"]]
}
})
mean(acc); mean(auroc); mean(sen); mean(spec); mean(prec)
##########################################################################
# svm
##########################################################################
library(e1071)
system.time({
acc <- c()
auroc <- c()
sen <- c()
spec <- c()
prec <- c()
for(seed in 1:10){
print(seed)
set.seed(seed)
data <- readRDS('result/thyroid_clean.rds')
index <- sample(unique(data$id))
index.list <- split(index, cut(seq_along(index), 10, labels = FALSE))
result <- data.frame()
for(index.test in index.list){
index.train <- setdiff(index, index.test)
test <- data[data$id%in%index.test,]
train <- data[data$id%in%index.train,]
#model <- svm(mal~., kernel='sigmoid', data=train, probability=T)
model <- svm(mal~., kernel='radial', data=train[,-1], probability=T)
#model <- svm(mal~., kernel='linear', data=train[,-1], probability=T)
pred.prob <- attr(predict(model, test[,-1], probability=T),'probabilities')[,1]
result <- rbind(result, cbind(row.names(test), pred.prob))
}
data[row.names(result), 'pred.prob'] <- result$pred.prob
data$pred.mal <- as.factor(ifelse(data$pred.prob >= 0.5, 1, 0))
cm <- confusionMatrix(data$pred.mal, data$mal, mode = 'everything', positive = '1'); cm[["overall"]][["Accuracy"]]
roc <- roc.curve(scores.class0 = data[data$mal=='1','pred.prob'],
scores.class1 = data[data$mal=='0','pred.prob'], curve=F); roc$auc
acc[seed] <- cm[["overall"]][["Accuracy"]]
auroc[seed] <- roc$auc
sen[seed] <- cm[["byClass"]][["Sensitivity"]]
spec[seed] <- cm[["byClass"]][["Specificity"]]
prec[seed] <- cm[["byClass"]][["Precision"]]
}
})
mean(acc); mean(auroc); mean(sen); mean(spec); mean(prec)