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Heart disease prediction;, Machine learning, Ensemble classifier, Hybrid Technique, Decision Tree, Naive Bayes, SVM, KNN, logistic regression, RF, Gradient Boosting, XGB
Cardiovascular disease is one of the top health concerns to humanity and is gradually increasing daily. Predicting it timely and taking the necessary steps for its intervention is crucial. Precisely predicting cardiac disease is a challenging job that a human or application can do. The complexity of the cardiovascular system compels the use of Artificial Intelligence (AI) to find the solution. Machine learning techniques (sub-set of artificial intelligence) have done tremendous work in medical sciences by providing vast answers to their queries. Computer scientists have used different machine-learning methods for the identification of cardiac disease. This study aims to enhance the accuracy of the prophecy of cardiac disease to reduce the risk factors. It proposes a hybrid ensemble framework to analyze the cardiac data based on essential features for optimum prediction results. This ensemble framework uses multiple machine-learning classification methods to approach the optimal solution. This study uses the Cleveland open access dataset to discuss the working performance of famous classification techniques like Decision Tree, Naive Bayes, SVM, KNN, logistic regression, RF, Gradient Boosting, and XGB Classifier. It proposes a Hybrid Ensemble Framework based on this analysis to enhance the results. The proposed method shows incredible results using the Adaptive Boosting Ensemble technique. AdaBoost is used with hyperparameters on the results retrieved from the applied ML methods and gets more accuracy. The accuracy of this proposed method is evaluated using an open-access Cleveland dataset, which has various cardiac modalities, clinical records, and physiological measurements. Our proposed Hybrid Ensemble Framework achieved an accuracy of 91.80%, precision= 0.94, f1-score=0.92, macro avg= 0.92, and recall = 0.93. The results obtained by the other machine-learning algorithms are less than our model. The comparison of previously completed results is also examined to reflect the improvement in the proposed technique. Moreover, this technique opens new doors for real-world clinical solutions, and it advances the cardiac disease risk stratification field by introducing an innovative and applicable approach by merging ML and ensemble methods. The HEF enhances prediction accuracy and provides valuable insights into the key factors influencing cardiac disease risk, ultimately facilitating more informed clinical decision-making. Our findings underscore the potential of this hybrid ensemble framework as a valuable tool for improving the detection and management of cardiac diseases, ultimately reducing the burden of CVD (cardiovascular disease) on healthcare systems and society.
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