HEART DISEASE PREDICTION USING MACHINE LEARNING TECHNIQUES

K. Yadav, Anurag Sharma, Abhishek Badholia
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引用次数: 77

Abstract

In few previous decades around the globe the reason for extensive number of deaths is cardiovascular disease or Heart related disease and not only in India but all around the world has emerged as a life-threatening disease. So for the correct treatment and in time diagnosis for this disease the need of feasible, accurate and reliable system is encountered. For automation of analysis of the sophisticated and huge data, to the various medical dataset of Machine Learning techniques and methods are applied. In recent times many researchers for the health care industry assistance with the help of various techniques of Machine Learning, this in turn helps the professionals in the procedure of the heart related disease diagnosis. A survey of various models that accepts such techniques and algorithms and their performance analysis is presented in this paper. Within the researchers few very fashionable Model supported supervised learning algorithms are Random forest (RF), Decision Tree (DT), Naïve Bayes, ensemble models, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM).  
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利用机器学习技术预测心脏病
在过去的几十年里,全球范围内大量死亡的原因是心血管疾病或心脏相关疾病,不仅在印度,而且在全世界都已成为一种危及生命的疾病。因此,要对本病进行正确的治疗和及时的诊断,就需要有一个可行、准确、可靠的系统。为了对复杂而庞大的数据进行自动化分析,对各种医疗数据集都应用了机器学习技术和方法。近年来,许多医疗保健行业的研究人员借助各种机器学习技术提供帮助,这反过来又帮助了心脏相关疾病诊断过程中的专业人员。本文综述了采用这种技术和算法的各种模型,并对其性能进行了分析。在研究人员中,一些非常流行的模型支持监督学习算法是随机森林(RF),决策树(DT), Naïve贝叶斯,集成模型,k -近邻(KNN)和支持向量机(SVM)。
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来源期刊
Information Technology in Industry
Information Technology in Industry COMPUTER SCIENCE, SOFTWARE ENGINEERING-
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