Abstract
With the shifting gears of lifestyle, large populations of the world are getting prone to the heart diseases. It is becoming one of the topmost reasons for loss of life. As the death rate is increasing due to coronary diseases, the people of healthcare department depend largely on the patient’s data to predict if the patient may have a risk of heart disease. Not every time can the doctors go through every minute detail of the data and predict accurately. It is time consuming and risky. The aim of the paper is to find best predicting algorithm which can help the non-specialized doctors or medical technicians in predicting the risk of disease. The prediction system uses different machine learning algorithms like logistic regression, support vector machine, k-nearest neighbor, Gaussian naïve Bayes, decision tree classifier and random forest classifier. The prediction accuracy for logistic regression is found to be the highest among all with 88.29% accuracy.
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Swain, D., Ballal, P., Dolase, V., Dash, B., Santhappan, J. (2020). An Efficient Heart Disease Prediction System Using Machine Learning. In: Swain, D., Pattnaik, P., Gupta, P. (eds) Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1101. Springer, Singapore. https://doi.org/10.1007/978-981-15-1884-3_4
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DOI: https://doi.org/10.1007/978-981-15-1884-3_4
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