Ensemble Machine Learning Approach for Detecting and Predicting Diabetes Mellitus Using Bagging and Stacking

The application of ensemble learning techniques more especially, bagging and stacking for better diabetes diagnosis and prediction is explored in this abstract. Due to their precision and individualized approach, advanced machine learning techniques are advantageous for diabetes, a complex metabolic illness. Here Pima Indian diabetes(PID) dataset for research, collected from the UCI machine learning Repository. The dataset contains information about 768 patients and their corresponding nine unique attributes. A novel ensemble machine learning technique for diabetes prediction such as bagging and a meta model is used in stacking to aggregate predictions from several models. A stacked model is created by combining Random Forest and Support vector machines with feature variables like age, body mass index, blood pressure, skin thickness and biochemical indicators. The accuracy of diabetes diagnosis and prediction has improved by bagging and stacking. They make it possible to identify high-risk patients early and to provide individualized therapies. This research has achieved better accuracy with random forest. As a result, the use of bagging and stacking in diabetes research has the potential to completely transform patient treatment by providing accurate risk assessments and individualized management plans. Realizing these methods' full potential in the treatment of diabetes will require further study, which will focus on improving them and addressing the problems they raise.