Prediction of Heart Attack Using Machine Learning

Department of Instrumentation & Control Engineering Bharati Vidyapeeth’s College of Engineering Delhi-110063 Department of Instrumentation & Control Engineering Bharati Vidyapeeth’s College of Engineering Delhi-110063 Department of Instrumentation & Control Engineering Bharati Vidyapeeth’s College of Engineering Delhi-110063 Department of Instrumentation & Control Engineering Bharati Vidyapeeth’s College of Engineering Delhi-110063 dDepartment of Instrumentation & Control Engineering Bharati Vidyapeeth’s College of Engineering Delhi-110063 Department of Instrumentation & Control Engineering Bharati Vidyapeeth’s College of Engineering Delhi-110063

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Published 2019-01-30

Pages 20-24

Abstract

Cardiovascular diseases are one of the biggest reasons for death of millions of people around the world only second to cancer. A heart attack occurs when a blood clot blocks the blood flow to a part of the heart. In case this blood clot cuts off the blood flow entirely, the part of the heart muscle begins to die as a result. Going by the statistics, a heart problem can gradually start between the age of 40-50 for people with unhealthy diet and bad lifestyle choices. So, an early prognosis can really make a huge difference in their lives by motivating them towards a healthy and active life. By changing their lifestyle and diet this risk can be controlled. This
Project intends to pinpoint the most relevant/risk factors of heart disease as well as predict the overall risk using machine learning. The machine learning model predicts the likelihood of patients getting a heart disease trained on dataset of other individuals. As the result, the probability of getting a heart disease based on current lifestyle and diet is calculated. The model was trained with Framingham heart study dataset.

Keywords

Heart Disease Machine Learning logistic regression Cross-validation

References

[1] Das, Turkoglu, and Sengur, “Efficient diagnosis of heart disease via machine learning models”, Expert systems with applications, 2009.
[2] Vanisree and Jyothi, “Decision Support model for Heart Disease prognosis based on early signs of 8–51 patients using binary classification”, International Journal of Computer Applications, 2011.
[3] Y. Zhang, “Studies on application of Support Vector Machines in coronary heart disease prediction model”, Electromagnetic Field Problems and Applications, Sixth International Conference (ICEF), IEEE 2012.
[4] Vadicherla and Sonawane, “Decision Support for coronary Heart Disease analysis Based on
[5] Minimal Optimization technique”, International Journal of Engineering Sciences and Emerging Technologies, 2013.
[6] H. Elshazly, Hassanien and Elkorany, “Lymph diseases prediction based on support vector machine algorithm”, Computer Engineering & Systems 9th International Conference (ICCES), 2014.
[7] Bhupender Kumar & Yogesh Paul, "Medical Applications of Machine Learning Algorithms", UIET, Kurukshetra University, 2016.
[8] ]Ram Avatar & Vineet Kumar, "Deep Learning in healthcare", UIET, Kurukshetra University, 2018.
[9] Xu, JQ, Murphy, SL., Kochanek, KD, Bastian, BA. Deaths: Final data for 2013. National Vital Statistics Report. 2016.
CDC. Million Hearts™: strategies to reduce the prevalence of leading cardiovascular disease risk factors. United States, 2011. MMWR 2011.

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How to Cite

Akshit Bhardwaj, Ayush Kundra, Bhavya Gandhi, Sumit Kumar, Arvind Rehalia, Manoj Gupta . (2019). Prediction of Heart Attack Using Machine Learning. IITM Journal of Management and IT, 10(1), 20-24.

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IITM Journal of Management and IT

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✓ Citation copied to clipboard

[1] [1] Das, Turkoglu, and Sengur, “Efficient diagnosis of heart disease via machine learning models”, Expert systems with applications, 2009.

[2] [2] Vanisree and Jyothi, “Decision Support model for Heart Disease prognosis based on early signs of 8–51 patients using binary classification”, International Journal of Computer Applications, 2011.

[3] [3] Y. Zhang, “Studies on application of Support Vector Machines in coronary heart disease prediction model”, Electromagnetic Field Problems and Applications, Sixth International Conference (ICEF), IEEE 2012.

[4] [4] Vadicherla and Sonawane, “Decision Support for coronary Heart Disease analysis Based on

[5] [5] Minimal Optimization technique”, International Journal of Engineering Sciences and Emerging Technologies, 2013.

[6] [6] H. Elshazly, Hassanien and Elkorany, “Lymph diseases prediction based on support vector machine algorithm”, Computer Engineering & Systems 9th International Conference (ICCES), 2014.

[7] [7] Bhupender Kumar & Yogesh Paul, "Medical Applications of Machine Learning Algorithms", UIET, Kurukshetra University, 2016.

[8] [8] ]Ram Avatar & Vineet Kumar, "Deep Learning in healthcare", UIET, Kurukshetra University, 2018.

[9] [9] Xu, JQ, Murphy, SL., Kochanek, KD, Bastian, BA. Deaths: Final data for 2013. National Vital Statistics Report. 2016.

[10] CDC. Million Hearts™: strategies to reduce the prevalence of leading cardiovascular disease risk factors. United States, 2011. MMWR 2011.