Prediction of Type-2 Diabetes Mellitus Disease Using Machine Learning Classifiers and Techniques - Ahamed, B. Shamreen; Arya, Meenakshi Sumeet; Nancy V, Auxilia Osvin (Frontiers in Computer Science, 2022)
The technological advancements in today’s healthcare sector have given rise to many innovations for disease prediction. Diabetes Mellitus is one of the diseases that has been growing rapidly among people of different age groups. There are various reasons and causes involved. These reasons are all considered as different attributes for the study. In order to predict the occurrence of Diabetes Mellitus (Type-2) disease, various ML algorithms can be used. The main motive of using the algorithm is to construct a predictive model to critically predict whether a person is affected by Diabetes. The classifiers taken are Logistic Regression (LR), XGBoost (XGB), Gradient Boosting (GB), Decision Trees (DT), ExtraTrees, Random Forest (RF) and Light Gradient Boosting Machine (LGBM). The dataset used is PIMA Indian Dataset sourced from UCI Repository. The performance of these algorithms are compared in reference to the accuracy obtained. The results obtained from the above mentioned classifiers show that LGBM Classifier has the highest accuracy of 95.20% in comparison with the other algorithms.