Application of Machine Learning Algorithm for Prediction of Diabetes and Heart Diseases

Nowadays, people are becoming more prone to health issues. Often, because of multiple health problems, one disorder leads to another. One such most predominant disorder is diabetes for which continuous blood glucose level monitoring is inevitable. This process is time-consuming and painful during the process. In addition, people with diabetes are more likely to experience heart diseases. To overcome this problem, we can utilize noninvasive methods to measure blood glucose level and certain other parameters like heart beat rate and percentage oxygen saturation to predict possibility of heart diseases. This paper presents implementing machine learning (ML) techniques in prediction of diabetes and heart diseases. The proposed method uses a noninvasive model to measure the necessary parameters that are compared with clinically accepted conventional measurements for its reliability. The dataset acquired is used to implement multiple ML algorithms, and their results are compared to identify the algorithm exhibiting highest accuracy.