Prediction of Diabetes Mellitus in Developing Countries: A Systematic Review

Diabetes Mellitus (DM) is a chronic disease that affects millions of people around the world, primarily in developing countries. Diabetes has also been found to be a recurrent and genetic and hereditary disease ravaging lives from one generation to another. Hence, early detection of diabetes is critical to avoiding complications and lowering healthcare costs. This review article aims to compare previous works that used various machine learning models in predicting Diabetes Mellitus (DM) in Nigeria, a developing country. The models' accuracy, precision, and recall, as well as their advantages and limitations, are all discussed. The difficulties encountered in applying machine learning models for DM prediction in Nigeria, as well as potential solutions, are also discussed. The article emphasizes the importance of ongoing research in this area as well as the advantages of using K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) models to improve early detection and management of diabetes in developing countries.