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Abstract

Cardiovascular Diseases (CVD) remain a leading cause of global mortality, necessitating accurate and scalable diagnostic tools. This study proposes a machine learning (ML)-based framework for early CVD prediction by integrating advanced feature selection, model tuning, and hybrid optimization techniques. Five ML models were evaluated, and their performance was enhanced through the application of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for hyperparameter optimization. Feature selection techniques were employed to identify clinically relevant predictors, including binning strategies for age, BMI, and MAP. PSO was applied within a cross validation (CV) loop, yielding a notable improvement in Logistic Regression (LR) performance raising CV accuracy from 81.5% to 82.51%. Similarly, GA optimization improved cross validation accuracy for Random Forest (RF) and XGBoost from baseline test accuracies of 82.03% and 81.1% to 82.51% and 82.04%, respectively. Evaluation metrics such as precision, recall, F1-score, and AUC were also utilized to ensure robust model assessment, especially considering imbalance within the dataset. A comparative analysis against traditional hyperparameter tuning methods (e.g., GridSearchCV) demonstrated the superiority of PSO and GA in enhancing predictive performance. However, the study is limited using of a single public dataset without external clinical validation. These findings highlight the necessity for more extensive data validation in subsequent studies, but they also highlight the promise of hybrid optimization and feature engineering in improving ML-based CVD diagnosis.

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