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- W4386804940 abstract "Heart disease is rapidly overtaking other causes of death in India, and it is a major threat to both men and women. Among the top causes of mortality throughout the globe, heart disease ranks first. Therefore, it is crucial to accurately and rapidly forecast cardiac problems at an early stage to guarantee the lives of millions of people. In the realm of medicine, machine learning has become more important. The realm of machine learning is where the idea of boosting first appeared. The underlying premise is that by merging multiple cases into a more accurate forecast, the accuracy of a rather ineffective classifying tool may be improved. Subsequently, this overarching idea was implemented in statistical modeling. The purpose of this research is to evaluate and contrast the performance of four popular boosting algorithms for machine learning in the context of cardiac illness diagnosis and prediction. Test results show that among the models evaluated, XGBoost performed best in terms of classification accuracy." @default.
- W4386804940 created "2023-09-17" @default.
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- W4386804940 date "2023-01-01" @default.
- W4386804940 modified "2023-09-27" @default.
- W4386804940 title "Diagnostic Classification of Heart Disease Using Boosting Algorithms" @default.
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- W4386804940 doi "https://doi.org/10.1007/978-981-99-3716-5_41" @default.
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