Review on Type II Diabetes Prediction Using Machine Learning Techniques

Diabetes mellitus is one of the major health problems in the world. Various research works haveattempted in predicting diabetes mellitus in general and type-2 diabetes using various machine learningtechniques such as Artificial Neural Networks, Support Vector Machine, Random Forest, and Decision Tree.The research work that was reviewed suggests that, using an ensemble of machine learning models providesmore accuracy in predicting diabetes. Due to the high accuracies provided by Random Forest and NeuralNetwork models in the analysed studies, we suggest that using Random Forest and Neural Networkcombined machine learning models will be able to give high prediction accuracy rates in future research.This study contributes to the literature by providing insights on which techniques can be most accurate indeveloping a machine learning model to predict diabetes mellitus, which can be embedded into ClinicalDecision Support Systems. This review paper discusses various approaches researchers have taken inpredicting diabetes mellitus and their results. Therefore, researchers could dive deep into the research thatis currently available and their progress by comparing the results.