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A Machine Learning Approach Towards Heart Attack Prediction
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Author(s): Ahona Ghosh (Maulana Abul Kalam Azad University of Technology, West Bengal, India), Moutushi Seth Sharma (Maulana Abul Kalam Azad University of Technology, West Bengal, India)and Sriparna Saha (Maulana Abul Kalam Azad University of Technology, West Bengal, India)
Copyright: 2022
Pages: 27
Source title:
Leveraging AI Technologies for Preventing and Detecting Sudden Cardiac Arrest and Death
Source Author(s)/Editor(s): Pradeep Nijalingappa (Bapuji Institute of Engineering and Technology, Davangere, India), Sandeep Kumar Kautish (Lord Buddha Education Foundation, Nepal), Mangesh M. Ghonge (Sandip Institute of Technology and Research Centre, India)and Renjith V. Ravi (MEA Engineering College, India)
DOI: 10.4018/978-1-7998-8443-9.ch006
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Abstract
Various types of heart diseases and conditions leading to increasing chance of heart attack have been a serious concern all over the world. Several factors like blood pressure, cholesterol, diabetes, obesity can affect the heart, and thus, those should be monitored regularly to prevent the chance of heart attack in people of different age groups. This chapter at first has analyzed different existing benchmarks of heart attack analysis. Being motivated by the shortcomings of the state-of-the-art literature and to address the challenges, it has introduced support vector machine, the most popular supervised machine learning algorithm to classify the chance of heart attack using a dataset downloaded from Kaggle. The experimental result has been evaluated using different performance metrics, including accuracy, error rate, precision, recall, F1 score. Finally, the performance has been compared with the existing related works also to validate its effectiveness and efficiency in real-time heart attack prediction.
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