G R - Research Scholar, Department of Electronics and Instrumentation Engineering, Karunya Institute of Technology and Sciences, ۶۴۱۱۱۴, India.
Mary X - Associate Prof., Department of Robotics Engineering, Karunya Institute of Technology and Sciences, ۶۴۱۱۱۴, India.
George S - Prof., Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, ۶۴۱۱۱۴, India.
Sagayam K - Assistant Prof., Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, ۶۴۱۱۱۴, India.
Fernandez-Gamiz - Prof., Department of Nuclear Engineering and Fluid Mechanics, University of Basque Country, Bilbao, ۴۸۹۴۰, Spain.
Günerhan - Associate Prof., Department of Mathematics, Kafkas University, Aiken, Kars, Turkey.
Uddin - Prof., Department of Business Administration, International Islamic University, Chittagong, ۴۳۱۸, Bangladesh.
Pramanik - Associate Prof., Department of Computer Science and Engineering, Haldia Institute of Technology, India.

Diabetes is a type of metabolic disorder with a high level of blood glucose. Due to the high blood sugar, the risk of heart-related diseases like heart attack and stroke got increased. The number of diabetic patients worldwide has increased significantly, and it is considered to be a major life-threatening disease worldwide. The diabetic disease cannot be cured but it can be controlled and managed by timely detection. Artificial Intelligence (AI) with Machine Learning (ML) empowers automatic early diabetes detection which is found to be much better than a manual method of diagnosis. At present, there are many research papers available on diabetes detection using ML techniques. This article aims to outline most of the literature related to ML techniques applied for diabetes prediction and summarize the related challenges. It also talks about the conclusions of the existing model and the benefits of the AI model. After a thorough screening method, ۷۴ articles from the Scopus and Web of Science databases are selected for this study. This review article presents a clear outlook of diabetes detection which helps the researchers work in the area of automated diabetes prediction.

Machine learning, diabetes, Classifiers, Prediction, Classification