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              <text>Predictive Modelling of Heart Disease: Exploring Machine Learning Classification Algorithms</text>
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              <text>Cross-validation; F1 Score; Grid Search; Heart attack prediction; Machine Learning; overfitting; Recall</text>
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              <text>In addressing the critical challenge of early and accurate heart failure diagnosis, this study explores the application of five machine learning models, including XGBoost, Decision Tree, Random Forest, Logistic Regression, and Gaussian Naive Bayes. Employing cross-validation and grid search techniques to enhance generalization, the comparative analysis reveals XGBoost as the standout performer, achieving a remarkable accuracy of 85%. The findings emphasize the significant potential of XGBoost in advancing heart failure diagnosis, paving the way for earlier intervention, and potentially improving patient prognosis. The study suggests that integrating XGBoost into diagnostic processes could represent a valuable and impactful advancement in the realm of heart failure prediction, offering promising avenues for improved healthcare outcomes.   2024 IEEE.</text>
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              <text>Yadav J.; Nair A.M.; George J.; Alapatt B.P.</text>
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              <text>International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>&lt;a href="https://doi.org/10.1109/ICDCOT61034.2024.10515398" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICDCOT61034.2024.10515398&lt;/a&gt;
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              <text>ISBN: 979-835038295-2</text>
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              <text>Yadav J., Christ (Deemed to Be University), Delhi NCR, India; Nair A.M., Christ (Deemed to Be University), Delhi NCR, India; George J., Christ (Deemed to Be University), Delhi NCR, India; Alapatt B.P., Christ (Deemed to Be University), Delhi NCR, India</text>
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