Abstract
The heart is the most vital organ found in the chest pit of people. Sudden blockage of blood flow to the heart causes a heart attack. Due to the lack of proper diagnosis and early stage prediction of heart disease, many people die every year. In today's era, the modern lifestyle and the polluted atmosphere are the main cause of growth in mortality rate. As per WHO data, cardiovascular diseases (CVDs) are the number one reason for death all around, taking an expected 17.9 million lives every year that is approximately 31% deaths around the globe. Irrespective of gender and age group, cardiovascular illness is a major issue in India. Hence, it is necessary that early prediction with accuracy can save a million lives. In this paper, different machine learning classification approaches are done for the early prediction of heart disease. After all, the conclusion is drawn that the random forest classifier produces more accurate predictions than other competitive approaches. It can be helpful for the necessary aid for doctors and chronic patients suffering from heart diseases.
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Mohapatra, S., Dash, J., Mohanty, S., Hota, A. (2021). An Approach for Heart Disease Prediction Using Machine Learning. In: Udgata, S.K., Sethi, S., Srirama, S.N. (eds) Intelligent Systems. Lecture Notes in Networks and Systems, vol 185. Springer, Singapore. https://doi.org/10.1007/978-981-33-6081-5_1
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DOI: https://doi.org/10.1007/978-981-33-6081-5_1
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