What do you suggest to increase the sensitivity rates in conventional ML model?

Question

I have an imbalanced data problem (prop. rate: 0.8571429 0.1428571) and for this reason, our sensitivity and PPV rates are very low. What do you recommend to fix this problem in R or in general? See the ML codes below,

new_data<-data # after the feature selection
index<- sample(n_rows, n_rows * 0.7)
trainset <- new_data[index,]
testset <- new_data[-index,]  
# imbalance data problem
train_sample <- ovun.sample(primary2 ~., data = trainset, method = "over", N = x5*2)$data
dim(train_sample) 
# ML model
mod_svm <- svm(primary2 ~ ., 
               data = train_sample, 
               type = 'C-classification',
               cost=15,kernel="radial")
#model summary
summary(mod_svm)
svm_pre <- predict(mod_svm,testset)
#confusion matrix
xx<-table(testset$primary2,svm_pre)
TN<-xx[1]
FN<-xx[2]
FP<-xx[3]
TP<-xx[4]
CV.SVM.Sensitivity=TP / (TP+FN)
CV.SVM.Specifity= TN/(TN+FP)
CV.SVM.PPV= TP/(TP+FP)
CV.SVM.NPV= TN/(TN+FN)
CV.SVM.Accuracy = (TP+ TN)/(TP+TN+FP+FN)
CV.SVM.Sensitivity
```