Diabetes Prediction Using Ensemble Learning

Using ensemble learning toward medical diagnostics as a response to diabetes on a global scale. The data set is composed of medical and demographic information collected from survey questionnaire forms filled out by patients; medical charts; and lab samples from diagnosed or at-risk subjects during patient clinic visits and hospitalizations. For instance, variables include age, sex, obesity, hypertension, ischemic heart disease, prior smoking status, post-prandial test blood, and random blood sugar levels in non-diabetic subjects. Rigorous data processing ensures reliability. Ensemble learning emphasizes the potential of predicting diabetes, thus, giving a more accurate forecast on the same and advanced prevention techniques. The approach is also useful in detailed research on the origins of diabetes and providing guidelines for prevention and treatment campaigns worldwide. The study reveals a highly accurate classification model with an overall accuracy of 95%. Precision is notable, with 95% for class 0 and 93% for class 1, while class 0 exhibits outstanding recall at 99%, whereas class 1 has a lower recall at 61%.