Precision in Diabetes Diagnosis: Harnessing Ensemble Methods in Machine Learning

Compared to affluent nations, diabetes is becoming more common, and the rate of increase is higher in countries with low and moderate incomes. Numerous difficulties, including foot troubles, skin problems, dental health problems, digestive disorders, bone and joint challenges, hearing impairments, sexual dysfunction, and mental health issues, can be brought on by uncontrolled diabetes. Among the most common illnesses worldwide, diabetes can be stopped in its tracks, and other problems can be avoided if it is discovered early on. In this study, we construct a prediction model that, depending on particular diagnostic metrics that are included in the collection, investigates different strategies to enhance the functionality and accuracy of the model. The objective of this study is to increase accuracy and reliability in the classification of diabetes by combining several different methods. Following the preprocessing of the dataset comprising clinical features associated with diabetes, boosting and bagging algorithms like AdaBoost and XGBoost are utilized separately. An ensemble technique is used to combine the predictions of various algorithms through voting to improve classification performance. By utilizing each algorithm’s unique strengths, the ensemble technique overcomes constraints and produces better outcomes. Accuracy, precision, recall, and F1-score act as subtle brushes in the complex world of machine learning evaluation, creating a unique picture of a model’s abilities, and cross-validation is used to gauge generalization. The findings indicate that the combined algorithm works better than the individual algorithms, providing improved diabetes classification and assisting with well-informed healthcare decisions.