Abstract
Heart disease, often known as Cardiovascular disease is one of the most lethal yet silent killers of humans, resulting in a rise in the mortality rate of sufferers per year. Every year, it kills nearly 17 million people worldwide in myocardial infarctions and cardiac attacks. Heart failure (HF) occurs when the heart cannot produce enough blood to satisfy the body’s needs. On the other hand, current risk prediction techniques are moderately effective because statistical analytic approaches fail to capture prognostic information in big data sets with multi-dimensional interactions.The research investigates the proposed AdaBoost ensemble technique with Synthetic Minority Oversampling Technique (SMOTE) on the medical reports of 299 heart failure patients obtained during their follow-up period at Faisalabad Institute of Cardiology (Punjab) and Allied Hospital Faisalabad (Pakistan), during April–December, 2015. The proposed approach builds on ensemble learning techniques such as adaptive boosting. It provides a decision support mechanism for medical practitioners to identify and forecast heart diseases in humans based on risk factors for heart disease. The efficacy of the proposed method validates by comparing various machine learning algorithms, and it is evident that the proposed method performs better with an accuracy of 96.34.
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Kameswara Rao, B., Prasan, U.D., Jagannadha Rao, M., Pedada, R., Kumar, P.S. (2022). Identification of Heart Failure in Early Stages Using SMOTE-Integrated AdaBoost Framework. In: Nayak, J., Behera, H., Naik, B., Vimal, S., Pelusi, D. (eds) Computational Intelligence in Data Mining. Smart Innovation, Systems and Technologies, vol 281. Springer, Singapore. https://doi.org/10.1007/978-981-16-9447-9_41
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DOI: https://doi.org/10.1007/978-981-16-9447-9_41
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