Identification of Diabetes Level Using AI/ML Model

Diabetes has become a critical public health challenge due to its alarming prevalence and severe complications. Predictive models are required for early diabetes detection to mitigate the impacts. The present study used K-nearest neighbor algorithm assessment regarding how age, body mass index, blood pressure, and physical activity affect diabetogenic risk. The different health data analyzed prove the effectiveness of K-NN for diagnosing those who are prone to diabetes. The model tries to classify people based on the above-named critical health indicators. Age and body mass index have an above average influence on blood pressure and amount of physical activity according to the K-NN model, thus this model could be used for effective stratification of risk related to diabetes as it scored 0.96. This confirms the utility of K-NN in providing very insightful information for assessing diabetes risk.