Machine Learning Analysis in the Prediction of Diabetes Mellitus: A Systematic Review of the Literature

Marres-Salhuana M.; Garcia-Rios V.; Cabanillas-Carbonell M. · Lecture Notes in Networks and Systems (2023) · DOI: 10.1007/978-981-19-1610-6_30

Resumen

In recent years, diabetes mellitus has increased its prevalence in the global landscape, and currently, due to COVID-19, people with diabetes mellitus are the most likely to develop a critical picture of this disease. In this study, we performed a systematic review of 55 researches focused on the prediction of diabetes mellitus and its different types, collected from databases such as IEEE Xplore, Scopus, ScienceDirect, IOPscience, EBSCOhost and Wiley. The results obtained show that one of the models based on support vector machine algorithms achieved 100% accuracy in disease prediction. The vast majority of the investigations used the Weka platform as a modeling tool, but it is worth noting that the best-performing models were developed in MATLAB (100%) and RStudio (99%).

Instituciones: Universidad Autónoma del Perú; Universidad Privada del Norte