Development of a Grey Wolf Optimized-Gradient Boosted Decision Tree Metamodel for Heart Disease Prediction
Keywords:
decision tree, heart disease, optimization, metamodel, machine learning, predictionAbstract
In this paper, a comprehensive study of various machine learning (ML) metamodels for heart disease detection is presented. The comparison includes conventional metamodels such as Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Trees, Random Forest as well as more advanced metamodels including Deep Learning, ML, Deep Neural Networks, Gradient Boosted Decision Trees and the proposed Grey Wolf Optimizer-Gradient Boosted Decision Trees (GWO-GBDT). The metamodels are assessed based on their performance in terms of accuracy, recall, precision, F1 measure and specificity. The results reveal that the developed GWO-GBDT metamodel outperforms the other metamodels in most metrics, offering superior prediction capabilities for heart disease diagnosis. This study provides a valuable reference for researchers and practitioners seeking efficient ML metamodels for heart disease prediction.
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