Detection of Risk Factors for Diabetes Mellitus with Machine Learning

Abstract

One of the most important diseases worldwide in public health is Diabetes Mellitus (DM) since this is one of the most severe and frequent non-communicable diseases with various chronic complications. In this paper, we propose a procedure to detect the most common risk factors in patients suffering from the disease known as diabetes mellitus, through principal components analysis (PCA) and non-negative matrix factorization (NMF). We then check the results using these factors like features, through the machine learning algorithms, improving the classification results. According to the experimental results, accuracy of more than 80% was obtained.