Enhancing Cardiovascular Disease Analysis in Healthcare Systems With Hybrid Random Forest and Neural Network Algorithm

Cardiovascular diseases (CVDs) remain a critical challenge in healthcare, requiring advanced analytical solutions for improved diagnosis and risk management. This study proposes a hybrid machine learning framework combining Random Forest (RF) and Neural Network (NN) algorithms to enhance both predictive accuracy and interpretability in CVD analysis. The hybrid approach leverages RF for effective feature selection and NN for capturing complex, nonlinear patterns in patient data. Using the UCI Heart Disease dataset, the hybrid model achieved a notable accuracy of 91%, precision of 92%, and recall of 89%. The system integrates Internet of Things (IoT)‐enabled real‐time monitoring capabilities, enhancing clinical decision support. Our findings demonstrate the model's superiority over standalone algorithms and underline its potential for personalized healthcare interventions. Future directions include real‐time model validation, scalability for multi‐disease prediction, and optimizing the model for resource‐constrained settings.