Exploring Hybrid Optimization Techniques for High-Performance Heart Disease Prediction Models: Challenges and Solutions
Cardiovascular disease (CVD) is currently among the leading causes of morbidity and mortality across the world, which is why there is an excellent demand of reliable, effective, and exact predictive mechanisms. In a typical machine learning/deep learning, the class imbalance, high-dimensional clinical data, noisy or redundant features, and computational inefficiencies are some of the key weaknesses that tend to deteriorate predictive reliability. To address these problems, scientists have resorted more and more to hybrid optimization methods, which combine machine learning models with metaheuristic methods in order to advance the process of feature selection, parameter optimization, and model generalization. In this survey paper, a detailed analysis of the hybrid optimization methods used to predict heart diseases is given, especially the way in which they improve the predictive capabilities. The paper examines popular metaheuristic algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) and explains their efficiency in the search of the most informative features of complicated medical data. Special focus is put on the classifier of the Random Forest that is optimized with the help of the following metaheuristic strategies in order to enhance precision and decrease overfitting. The survey identifies the practical challenges of hybrid optimization, including the cost of computation, convergence problems, and the heterogeneous healthcare data, as well. These limitations are mitigated by suggesting possible solutions and research directions. Lastly, the paper provides a comparison of the performance of different machine learning algorithms that have been optimized using different metaheuristic methods and it is established that hybrid models will always have a high predictability of cardiovascular disease. The results emphasize the relevance of hybrid optimization as a strong direction in the formation of high-performance and clinically reliable models of heart disease prediction.