Detection Diabetes Mellitus Based on Anthropometric Parameters and Machine Learning Techniques

ic mellitus (T2DM). According to recent data, T2DM, which is mostly associated with visceral or abdominal obesity and metabolic abnormalities, is more common in patients with metabolic syndrome. . Identifying those who are at high risk for type 2 diabetes is vital given the disease's prevalence and serious complications. Anthropometric assessment techniques are one of the most straightforward and non-invasive ways to detect individuals at risk for diabetes, even though one-third of patients with the disease have not been identified, and this number is rising. Diabetes is predicted by the Waist-to-Height Ratio (WHtR), Body Adiposity Index (BAI), A Body Shape Index (ABSI), Body Mass Index (BMI), and Waist Circumference (WC). According to one study, the best indicators are WC and BMI. Few researches have compared the values of these anthropometric indices and their relationship with the prevalence of diabetes, despite the fact that there have been several studies on the topic. Additionally, visceral and subcutaneous fat cannot be detected using conventional anthropometric techniques. We chose to design a cohort study to investigate additional and novel anthropometrical measures for assessing their association with diabetes, given the rising prevalence of the disease, the dearth of accurate measurements for its diagnosis, and the controversy surrounding the findings of earlier studies. In order to identify patients at risk of acquiring diseases, we also employed machine learning techniques, which are revolutionary and efficient ways to organize a huge number of indicators while creating powerful predictive models.