Enhanced Patient Risk Stratification Using Random Forest and Neural Network Ensembles in Machine Learning

Abstract

This research paper investigates the development of an enhanced patient risk stratification model utilizing the synergistic power of Random Forest and Neural Network ensembles. The primary objective is to improve predictive accuracy and robustness in identifying high-risk patients in clinical settings. The study leverages a comprehensive dataset encompassing diverse patient demographics, clinical histories, and treatment outcomes to ensure generalizability and applicability across different healthcare environments. We implement a hybrid ensemble model that combines the strengths of Random Forest’s decision-tree-based approach, which excels in handling high-dimensional data and capturing complex interactions, with Neural Networks' ability to model non-linear relationships and adapt to evolving patterns. The ensemble method is evaluated against traditional models using metrics such as the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity, specificity, and F1-score. Results indicate a significant improvement in stratification accuracy, with the ensemble model outperforming standalone methods. Moreover, the hybrid framework demonstrates better generalizability and robustness, maintaining high performance across subgroups with varying baseline risks. This study underscores the potential of advanced machine learning techniques in enhancing patient risk stratification, thereby facilitating early intervention and informed decision-making in clinical practice. The paper concludes with a discussion on the implications of these findings for healthcare delivery and future research directions, including the integration of real-time data and personalized medicine approaches.