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AUTHOR(S): Sudha, Irfan Ahmed
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ABSTRACT Qualitative study of information related to myocardial infarction patients is essential to prevent sudden cardiac death. Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide. Revolutionizing mortality and readmission prediction in heart failure by overcoming limitations of traditional models and using predictive models based on machine intelligence provides crucial information for decision making. A data driven approach for addressing the clinical and public health challenges of heart failure play an important role in patients following angioplasty. However, precisely predicting outcomes in heart failure patients remains difficult. There is a great need to develop and validate data-driven predictive models supporting this purpose. Recently, artificial intelligence (AI) methods have been successfully implemented in several medical fields. The same applies to the heart failure population. A comparative analysis of various predictive algorithms using machine intelligence and expedition through various models will give researchers insights into prevalence, hospitalizations, and global impact of predictive modeling for enhanced risk stratification and prognosis. |
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KEYWORDS Machine intelligence, Neural networks, Predictive models, Myocardial infarction Angioplasty |
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Cite this paper Sudha, Irfan Ahmed. (2024) Machine Learning in Cardiology: Comparative Analysis of Post Angioplasty Myocardial Infarction Prediction Models. International Journal of Biology and Biomedicine, 9, 16-23 |
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