Ensemble Machine Learning Framework for Heart Abnormality Classification with Effective Feature Selection

Coronary artery disease (CAD) is the most common cardiovascular disease. Risk factors impact CAD progression. Diagnostic and therapy methods for this illness include several costly side effects. Consequently, researchers are seeking economical and precise techniques for diagnosing this condition. Machine learning algorithms may assist doctors in the early diagnosis of the condition. Hence this work presents an efficient approach for feature selection and classification of abnormal heart rate patterns by combining Joint Mutual Information (JMI), Quantum Annealing, and a Bayesian ensemble model using CatBoost and XGBoost classifiers. The method starts with Joint Mutual Information to rank features based on their dependency with the target variable, identifying the most informative features for classification. Quantum Annealing, specifically simulated annealing in this case, is then used to optimize the subset of features by exploring the feature space and selecting the most relevant combinations, thus improving the model's performance by avoiding suboptimal solutions. The selected characteristics are then input into a Bayesian ensemble of CatBoost and XGBoost classifiers, which are trained to forecast heart rate irregularities. This ensemble method integrates the advantages of gradient boosting models to improve forecast accuracy and mitigate overfitting. The proposed technique is termed Probability-based Bayesian Statistics with Ensemble Boost (PBSEnsBoost), achieving an accuracy of 98.2%, specificity of 93.6%, sensitivity of 92.6%, and an F1-score of 95.7%.