Comparative Analysis of Meta-Heuristic Optimization–Enhanced Machine Learning Models for Heart Failure Prediction

In this paper, metaheuristic optimization algorithms (Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Simulated Annealing (SA), and Ant Colony Optimization (ACO)) are used to improve the performance of machine learning (ML) models in terms of early detection of heart failure. Core ML models like the Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting Machines (GBM), Decision Trees (DT) and the Artificial Neural Networks (ANN) are tested in their raw forms and again optimized through heuristic algorithms, on two publicly available datasets (the UCI Heart Disease Dataset and the Kaggle Heart Disease Dataset). The findings indicate that heuristic optimization, especially the RF-SA (Random Forest - Simulated Annealing) model, with 91.30 and F1-score of 0.9047 on DATASET-1, has the best results. Besides, the RF and SVM-PSO models achieve an accuracy of 91.30 on the Heart dataset, which is significantly higher in predictive accuracy when using optimized hybrid models. The impact of optimization on the generalizability of the model is seen upon the comparative analysis of various datasets. This paper provides one of the key contributions to building more valid and precise heart failure prediction systems.