Prediction Model for Prevalence of Type-2 Diabetes Mellitus Complications Using Machine Learning Approach

Nowadays, most of the people are suffering from the attack of chronic diseases because of their lifestyle, food habits, and reduction in physical activities. Diabetes is one of the most common chronic diseases being suffered by the people of all ages. As a result, the healthcare sector is generating extensive data containing huge volume, enormous velocity, and a vast variety of heterogeneous sources. In such scenario, scientific solutions offer to harness these massive, heterogeneous and complex datasets to obtain more meaningful information. Moreover, machine learning algorithms can play a tremendous part in creating a statistical prediction-based model. The aim of this paper is to identify the prevalence of diabetes related to long-term complications among patients with type-2 diabetes mellitus. The processing and statistical analysis require machine learning environment known as Scikit-Learn, Pandas for Python, and R-Studio for R. In this work, machine learning approaches such as decision tree, random forest for developing classification system-based prediction model to assess type-2 diabetes mellitus chronic diseases have been studied. Additionally, we have proposed an algorithm which is solely based on random forest and tried to detect the complicated areas of type-2 diabetes patients.