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Healthcare is evolving from standard to personalized, driven by the patients’ needs. Personalized healthcare is a medical model based on genetics, genomics, and other biological information that helps to predict risk for disease. To date, machine learning and data mining are the fastest-growing healthcare field used to classify patient cohorts from a large dataset and its application for diabetes subtyping will be a breakthrough. In this review paper, we have identified, analyzed, and summarized how previous studies distinguished diabetes into subtypes besides implementing the methods for diabetes subtyping using data mining and various clustering algorithms. We have discovered that many studies have suggested diabetes can be differentiated into subtypes clinically based on the risk complications, genetically defined, using clinical features, and for treatment selection. As for clustering algorithms, k-means clustering and hierarchical clustering were shown to be widely used in determining sub-clusters of diabetes. To further investigate diabetes subtyping, understanding the specific objective and method of diabetes subtyping using clustering algorithms from a large dataset will be crucial which could contribute to novel knowledge and improvement for diabetes management.
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