Enhancing Chronic Disease Management through Machine Learning: A Comparative Analysis of Random Forest and Neural Network Predictive Models

Abstract

This research paper investigates the application of machine learning techniques to improve chronic disease management, focusing on a comparative analysis between Random Forest and Neural Network predictive models. Chronic diseases, such as diabetes and cardiovascular disorders, represent a significant burden on healthcare systems, necessitating innovative approaches to enhance predictive accuracy and patient outcomes. The study evaluates the efficacy of these models using a large dataset comprising medical records and health indicators from diverse patient cohorts. Key performance metrics, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC), are employed to assess the models' capabilities in predicting disease exacerbations. Preliminary results indicate that both models offer substantial improvements over traditional statistical methods, with the Neural Network demonstrating superior performance in handling nonlinear relationships and complex feature interactions. The Random Forest model, however, exhibits greater interpretability and robustness in managing missing data and providing variable importance measures. These findings underscore the potential of integrating machine learning models into clinical decision support systems, offering clinicians data-driven insights to personalize treatment plans, minimize complications, and enhance overall patient care. The paper discusses the implications of these results for future research and the challenges of deploying machine learning solutions in real-world clinical settings.