使用不同的机器学习算法预测心脏病

Arkadeep Bhowmick, Kapil Dev Mahato, Chandrashekhar Azad, U. Kumar
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引用次数: 1

摘要

心脏病(HD)病例日益增多,因此及早发现是非常重要的。近年来,机器学习算法(MLA)正在成为医疗保健领域心脏或心血管疾病预测的趋势。强化、无监督和监督等数据挖掘技术在检查医疗领域行业的大量数据方面发挥着至关重要的作用。利用UCI库Cleveland数据库中HD个体的可用数据集对MLA的性能进行了测试和验证。本文通过执行不同的MLA,例如决策树(DT)、随机森林(RF)和逻辑回归(LR),对HD进行了早期预测。经过三种算法的对比研究,我们发现DT算法是最有效的算法,准确率最高,达到94.7%。这一数值高于最近报道的83.87%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Heart Disease Prediction Using Different Machine Learning Algorithms
Heart disease (HD) cases are increasing rapidly every day, so it is very crucial to detect them beforehand. In recent times, machine learning algorithms (MLA) are trending for heart or cardiovascular disease prediction in the healthcare field. Data mining techniques such as reinforcement, unsupervised, and supervised play a crucial role in examining the enormous amount of data in the medical field industry. The available dataset of HD individuals from the Cleveland database of the UCI repository is employed to test and verify the performance of MLA. This article makes an early prediction of HD by executing different MLA, for example, decision tree (DT), random forest (RF), and logistic regression (LR). After the comparative study of three algorithms, we found that the DT is the most efficient algorithm with the highest accuracy of 94.7 percent. This value is higher than the recently reported value of 83.87 percent.
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