Leveraging Data to Solve for Non-communicable Disease [Diabetes] and Healthcare Delivery using Machine Learning Techniques

Diabetes is rapidly becoming one of the major diseases in India, causing serious problems like heart disease, kidney failure, and nerve damage. The prevalence of diabetes is expected to increase by 2040, and early detection is vital for effective management and reducing long-term treatment costs. In this study, we used a learning model to improve the accuracy of diabetes prediction by analyzing key health indicators like blood sugar level, BMI, insulin, and age. Various classification methods were used, including logistic regression, decision trees, random forest, support vector machine (SVC), Gaussian Naive Bayes, K-nearest neighbor, Ada Boost, Bag, gradient boosting, and voting. To improve the performance of the prediction, we combine the results of each model and select the prediction frequency as the final result. The analysis found that random forest and voting models performed better, but the combined method was more reliable in early diabetes diagnosis. This study highlights the transformative potential of machine learning in the future development of healthcare in India, providing datadriven solutions to combat the challenges of increasing diabetes. The burden of diabetes could impact healthcare by 2040 if not addressed, and advances like these are important for the future of care in this country.