Hi, I've been trying to use different ML algorithm to improve the performance of my model, so far, the sensitivity is quite low with the highest stagnating around 20%. Please, what should I do to improve the model performance?

```
split <- sample.split(ml$Status, SplitRatio = 0.75)
train <- subset(ml, split == TRUE)
test <- subset(ml, split == FALSE)
#as.data.frame(table(train$Status))
train$Status <- as.factor(train$Status)
train <- SMOTE(Status ~., train, perc.over = 350,perc.under = 161)
round(prop.table(table(dplyr::select(train, Status), exclude = NULL)),4)
#...........................................................................
#Model Development
## fit a logistic regression model with the training dataset
log.model <- glm(Status ~., data = train[-c(5)], family = binomial)
summary(log.model)
colnames(test)
test[1:8,]
#.............................................................................
## to predict using logistic regression model, probabilities obtained
log.predictions <- predict(log.model,test, type="response")
logit.pred <- as.factor(ifelse(log.predictions > 0.5, 1, 0))
## Look at probability output
head(logit. pred, 10)
#..............................................................................
#View the confusion matrix of logistic regression.
#table(logit.pred,test[,8])
caret::confusionMatrix(as.factor(logit.pred), as.factor(test$Status), positive = "1")
#(final_df <- cbind(model_input_df[, c(1,2)], "pred_values" = logit.pred))
#head(final_df)
```