Predictive Modeling in Healthcare for Integrating SVM and Decision Trees for Claims Cost Management

Abstract

Predictive modeling in healthcare has emerged as a powerful tool for managing claims costs and optimizing resource allocation. This study proposes a hybrid approach that integrates Support Vector Machines (SVM) and Decision Trees (DT) to forecast healthcare claims costs accurately. The growing complexity of healthcare claims management necessitates the development of robust and interpretable predictive models. By leveraging the strengths of SVM's ability to handle high-dimensional data and DT's interpretability, the proposed model aims to provide superior accuracy and reliability in claims cost prediction. The study utilizes real-world healthcare datasets to evaluate the performance of the hybrid SVM-DT model and compares it with conventional methods. The results demonstrate improved forecasting capabilities, highlighting the potential of machine learning techniques in addressing the challenges of claims cost management. The insights gained from this predictive modeling approach can assist healthcare insurers and providers in minimizing financial risks, optimizing healthcare delivery, and enabling data-driven decision-making. The study contributes to the growing body of research on the application of machine learning in healthcare, emphasizing the importance of integrating multiple techniques to enhance the accuracy and interpretability of predictive models. The findings have implications for stakeholders seeking to improve the efficiency and sustainability of healthcare systems in the face of rising costs and complex claims management processes.