Enhancing Healthcare: Machine Learning for Diabetes Prediction and Retinopathy Risk Evaluation

Abstract

Diabetes mellitus stands as a major public health issue that affects millions globally. Among the various complications associated with diabetes, diabetic retinopathy presents a significant concern, affecting approximately one-third of diabetic patients. Early detection of diabetic retinopathy is paramount, as timely treatment can significantly reduce the risk of severe visual impairment. The study employs advanced machine learning techniques to predict diabetes and assess risk levels for retinopathy, aiming to enhance predictive accuracy and risk stratification in clinical settings. This approach contributes to better management and treatment outcomes. A diverse array of machine learning models including Logistic Regression, Random Forest, XGBoost, voting classifiers was used. These models were applied to a meticulously selected dataset, specifically designed to include comprehensive diabetic indicators along with retinopathy outcomes, enabling a detailed comparative analysis. Among the evaluated models, XGBoost demonstrated superior performance in terms of accuracy, sensitivity, and computational efficiency. This model excelled in identifying risk levels among diabetic patients, providing a reliable tool for early detection of potential retinopathy. The findings suggest that the integration of machine learning models, particularly XGBoost, into the healthcare system could significantly enhance early screening and personalized treatment plans for diabetic retinopathy. This advancement holds the potential to improve patient outcomes through timely and accurate risk assessment, paving the way for targeted interventions.