Open AccessPredicting Type 2 Diabetes A Machine Learning Approach
Author(s) -
Hao Lian,
Mafas Raheem,
Seetha Letchumy
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b3484.079220
Subject(s) - random forest , naive bayes classifier , machine learning , artificial neural network , artificial intelligence , computer science , support vector machine , type 2 diabetes , classifier (uml) , diabetes mellitus , medicine , endocrinology
Diabetes is a well-known common disease among people around the world. Diabetes causes many anomalies in the body and results in the patients to become under a long term medication. Detecting diabetes has been done via hectic medical tests and causes a delay for the patients to get to know their test results. However, data mining and machine learning approaches are in the frontline supporting the health care domain to make effective predictions in this regard. This paper elaborates about predicting Type 2 Diabetes Mellitus using classification models. A suitable secondary dataset was used to build classification models and the more suitable model was selected via the valid performance measures. In this line, the Random Forest, Support Vector Machine, Naïve Bayes and Artificial Neural Network models were built. Based on the performance measures, Random Forest has been identified as the more suitable classifier with the accuracy of 90%, the recall and precision value of 0.90.