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- W4377993157 abstract "The heart is an essential organ in the human body. It can be attributed to birth problems, heredity, or may even be due to our health routine. Therefore, it has become exceedingly tough for healthcare practitioners to detect and anticipate cardiovascular problems at an early stage considering several criteria such as an aberrant pulse rate or excessive BP. Which has resulted in a desperate need for an effective and reliable technique to detect them, considerably expanding with each passing day. Five Machine learning algorithms Support Vector Machine, K-Nearest Neighbour, Logistic regression, Random Forest, and Artificial Neural Network are used to operate on massive datasets gathered from the healthcare industry, anticipating, and aiding in the decision-making process. The predicted outputs are based on features like Age, Blood Pressure rate, Cholesterol, Glucose, Smoke, Alcohol, etc. The features necessary for this model are basic enough for any ordinary non-medical individual to determine whether they are at risk for any cardiac disorders with a simple blood scan rather than a medical evaluation. We achieved an accuracy of 99.8% by using the K-NN algorithm which is greater than any of the previous articles." @default.
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- W4377993157 date "2023-01-01" @default.
- W4377993157 modified "2023-09-27" @default.
- W4377993157 title "Cardiac Anomaly Detection Using Machine Learning" @default.
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- W4377993157 doi "https://doi.org/10.1007/978-3-031-27409-1_79" @default.
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