Diabetes Prediction using Random Forest Classifier with Different Wrapper Methods

Diabetics Mellitus is a common disease in the real world. It creates major health issues. Predicting diabetes in an early stage can save a human life. Health care contains huge data to access data with machine learning techniques. Diabetes occurs due to a lack of physical work and food habits. This research study predicts diabetes by using Pima Indian Dataset. This paper focuses on the results of the Random Forest (RF) classifier along with different wrapper methods. The Hyperparameter tuning method shows the more accuracy when compared to Random Forest (RF) classifier.