Abstract
Now a day’s heart disease is emerging as one of the most death-dealing diseases. As per a report published by the World Health Organization [WHO], heart disease is one of the most hazardous diseases to human which causes death all over the world from the last 20 years. Approx. 12 million people are dying every year, which makes it the biggest challenge for medical professionals to develop an early diagnosis of heart disease with better accuracy. In this paper, we have applied different machine learning algorithms and compared their classification accuracies. We have proposed a modified algorithm using logistic regression with principal component analysis for predicting heart disease with more accuracy on various attributes such as age, blood pressure, chest pain, serum cholesterol levels, heart rate, and other characteristic attributes, and patients will be classified according to varying degrees of coronary artery disease.
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03 September 2020
The original version of the book was inadvertently published with an incorrect author name in chapter 45. The chapter and book have now been updated with the changes.
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Ansari, M.F., Alankar, B., Kaur, H. (2021). A Prediction of Heart Disease Using Machine Learning Algorithms. In: Chen, J.IZ., Tavares, J.M.R.S., Shakya, S., Iliyasu, A.M. (eds) Image Processing and Capsule Networks. ICIPCN 2020. Advances in Intelligent Systems and Computing, vol 1200. Springer, Cham. https://doi.org/10.1007/978-3-030-51859-2_45
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