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Sumit Halder
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- Student, Department of Computer Science and Engineering, Greater Kolkata College of Engineering and Management (GKCEM), Kolkata, India
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Abstract
nHeart attacks have become a prevalent and serious condition in recent years due to a variety of causes. Numerous variables, including age, sex, fat, and others, can be used to predict it. In the current study, it was found that a data set with 13 parameters and 302 distinct data values, collected from a Kaggle dataset to assess patient condition, was covered. This article delves into the application of machine learning and artificial intelligence algorithms for the prevention of heart disease. The primary objective of this study is to enhance the accuracy and precision of identifying cardiac ailments by utilizing advanced algorithms. These algorithms play a pivotal role in discerning the presence or absence of cardiac issues within a given case. By harnessing the power of these technologies, the article aims to revolutionize cardiac complaint detection, ultimately contributing to early intervention and improved patient outcomes in the realm of heart disease management.
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Keywords: Artificial Intelligence, Machine Learning, Regression, SVM, Logistic Regression
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References
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International Journal of Biomedical Innovations and Engineering
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| Volume | 01 |
| Issue | 01 |
| Received | July 13, 2023 |
| Accepted | July 30, 2023 |
| Published | August 10, 2023 |
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