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Type 2 diabetes mellitus (T2DM) is a chronic disease characterized by elevated blood glucose levels and insulin resistance, leading to multiple organ damage with implications for quality of life and lifespan. In recent years, the rising prevalence of T2DM globally has coincided with the digital transformation of medicine and healthcare, including extensive electronic health records (EHRs) for patients and healthy individuals. Numerous research articles as well as systematic reviews have been conducted to produce innovative findings and summarize current developments and applications of data science in the life sciences, medicine and healthcare. The present review is conducted in the context of T2DM and Machine Learning, examining relatively recent publications using tabular data and demonstrating the relevant use cases, the workflows during model building and the candidate predictors. Our work indicates that Gradient Boosting and tree-based models are the most successful ones, the SHAPley and Wrapper algorithms being quite popular feature interpretation and evaluation methods, highlighting urinary markers and dietary intake as emerging diabetes predictors besides the typical invasive ones. These results could offer insight toward better management of diabetes and open new avenues for research.
Petridis et al. (2024) studied this question.
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