Heart Disease Prediction Using Machine Learning Techniques

S. Guruprasad, Valesh Levin Mathias, Winslet Dcunha
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Abstract

Consequent to the modern world life style and the increase in heart diseases every year, people’s lives are at risk. Heart diseases have become one of the most common reasons for fatalities these days, including in the young. Hence it has become very necessary to search and find the simplest and best solutions to predict the risk of getting these diseases in advance so that necessary steps can be taken to save the lives of people. This paper tries to find and produce a solution using certain medical datasets that could help predict the risk of heart diseases based on various parameters to find the percentage and level of risk of a patient. The predicted results can be used as a basis for the further steps that the user will choose to take and thus reduce unnecessary costs such as tests, scans and other expenses in many cases. The overall objective of our work is to predict the chances of getting a heart disease with few tests and attributes for the presence of heart diseases
使用机器学习技术预测心脏病
由于现代世界的生活方式和每年心脏病的增加,人们的生命处于危险之中。如今,心脏病已成为最常见的死亡原因之一,包括年轻人。因此,有必要寻找最简单和最好的解决方案,提前预测患这些疾病的风险,以便采取必要的步骤来挽救人们的生命。本文试图找到并产生一个解决方案,使用某些医疗数据集,可以帮助预测心脏病的风险基于各种参数,以找到患者的风险百分比和水平。预测结果可以作为用户选择采取的进一步步骤的基础,从而在许多情况下减少不必要的成本,例如测试、扫描和其他费用。我们工作的总体目标是通过很少的测试和心脏病存在的属性来预测患心脏病的几率
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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