Abstract
In the modern world, heart disease is one of the leading causes of death. It has a large global mortality rate, and it now poses a risk to many people’s health. Examination of medical data faces a serious issue with heart disease prediction. It has been demonstrated that machine learning (ML) is useful for aiding in the forecast and making of conclusions drawn from the enormous amount of data that the medical sector produces. In this work, we recommend an innovative method to recognize the large increment in predictive accuracy using ensemble learning techniques. We accurately produce a higher performance level. Machine learning (ML) can offer a practical remedy for decision-making and precise forecasts. The medical sector is demonstrating enormous advancement in the application of machine learning methods. The proposed work suggests using an ensemble machine learning technique to predict cardiac disease. Finally, based on the soft voting ensemble model, our method yielded an accuracy of 89.67%.
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Gavali, P.R., Bhosale, S.A., Sangar, N.A., Patil, S.R. (2023). Ensemble Learning Approach for Heart Disease Prediction. In: Shukla, P.K., Mittal, H., Engelbrecht, A. (eds) Computer Vision and Robotics. CVR 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-4577-1_4
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DOI: https://doi.org/10.1007/978-981-99-4577-1_4
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