Early prediction of diabetes mellitus using various artificial intelligence techniques: a technological review

Millions of people around the globe are suffering from diabetes. Most of the patients (diabetic or potentially diabetic) are not familiar with their health issues and the risk factor they face before the diagnosis of diabetes. The paper reviews substantial work related to diabetes mellitus based on different classification techniques. In this paper, a generic smart framework for realistic health management of diabetes mellitus is presented and implemented using a publically available Pima Indian diabetes dataset sourced from the UCI machine learning repository. Different classification algorithms were employed namely decision tree (DT), random forest (RF), eXtreme gradient boosting (XGB), AdaBoost (AB) and gradient boosting classifier (GBC). Pre-processing techniques have been employed to improve the data quality assessment. Among all the classifiers, GB outperformed other models with accuracy rate of 92.20% followed by RF, XGB, ADB and DT as 91.55%, 89.61%, 89.61% and 88.96%, respectively.