NDM-Finder: A Machine Learning Based Approach for Type-2 (Neonatal) Diabetes Mellitus Prediction

Abstract

Type 2 diabetes mellitus is a severe disease in which the pancreas' insulin does not act correctly. In the United Kingdom, type 2 diabetes affects around 90% of diabetics. It is a severe ailment that might last a lifetime. Type 2 diabetes has no known cure. However, with the proper diagnosis at an early stage, type 2 diabetes may be managed, and the chance of getting it is reduced. In this research, machine learning has been applied to detect the presence of type 2 diabetes in patients. Exploratory Data Analysis has been performed to uncover the insights of the type 2 diabetes prediction dataset. Several classification algorithms - Support Vector Machine, Random Forest, and XGBoost algorithm were applied, and then feature importance scores were also computed to understand the feature impact on the development of the machine learning model. XGBoost model achieved better execution in different metrics like accuracy (100%), precision (100%), and recall (100%) and outperformed previous works.