A Scoping Review of Supervised Machine Learning Techniques in Predicting the Prevalence of Type 2 Diabetes Mellitus

It is crucial for medical practice to navigate from solely dependent on conventional data-analytical approaches for disease screening, diagnostics, and treatment plans to decisions that are configured rapidly through big data analytics from artificial intelligence algorithms. The fog- and edge-computing architectures built within the huge healthcare database systems would allow the applications of machine learning (ML) algorithms for disease predictions and forecasting capacities. This scoping review appraised the use of multiple ML methods for type 2 diabetes mellitus (T2DM) prediction. Search engines used were IEEE Xplore, JSTOR, PubMed, Sage, Scopus, Wiley, and WOS. Inclusion criteria included articles published within the past six years, open access and studies that focused on T2DM only. Out of 41 studies included, the most used ML method was Random Forest (n=33) and the most occurred best ML model (n=13). Customised Ensemble ML method adapted to the dataset was found to show the highest accuracy. However, there were insufficient study areas and samples in Southeast Asia countries, as there were differences in demographics and culture that affect the T2DM risk factors where computational resource and systems development were limited. We conclude ML methods can predict T2DM, from the system’s perspective its intra-operability is viable for use in healthcare systems.