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Objectives: This study describes an unsupervised machine learning approach used to estimate the homeostatic model assessment-insulin resistance (HOMA-IR) cut-off for identifying subjects at risk of IR in a given ethnic group based on the clinical data of a representative sample. Methods: The approach was applied to analyse the clinical data of individuals with Arab ancestors, which was obtained from a family study conducted in Nizwa, Oman, between January 2000 and December 2004. First, HOMA-IR-correlated variables were identified to which a clustering algorithm was applied. Two clusters having the smallest overlap in their HOMA-IR values were retrieved. These clusters represented the samples of two populations, which are insulin-sensitive subjects and individuals at risk of IR. The cut-off value was estimated from intersections of the Gaussian functions, thereby modelling the HOMA-IR distributions of these populations. Results: A HOMA-IR cut-off value of 1.62 ± 0.06 was identified. The validity of this cut-off was demonstrated by showing the following: 1) that the clinical characteristics of the identified groups matched the published research findings regarding IR; 2) that a strong relationship exists between the segmentations resulting from the proposed cut-off and those resulting from the two-hour glucose cut-off recommended by the World Health Organization for detecting prediabetes. Finally, the method was also able to identify the cut-off values for similar problems (e.g. fasting sugar cut-off for prediabetes). Conclusion: The proposed method defines a HOMA-IR cut-off value for detecting individuals at risk of IR. Such methods can identify high-risk individuals at an early stage, which may prevent or delay the onset of chronic diseases such as type 2 diabetes.
Keywords: Unsupervised Machine Learning; Cluster Analysis; Insulin Resistance; Diabetes Mellitus, Type II.
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