Heart Disease Prediction Using Frequent Item Set Mining and Classification Technique

Sinkon Nayak, Mahendra Kumar Gourisaria, M. Pandey, S. Rautaray
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引用次数: 14

Abstract

The heart is the most important part of the human body. Any abnormality in heart results heart related illness in which it obstructs blood vessels which causes heart attack, chest pain or stroke. Care and improvement of the health by the help of identification, prevention, and care of any kind of diseases is the main goal. So for this various prediction analysis methods are used which job is to identify the illness at prelim phase so that prevention and care of heart disease is done. This paper emphasizes on the care of heart diseases at a primitive phase so that it will lead to a successful cure. In this paper, diverse data mining classification method like Decision tree classification, Naive Bayes classification, Support Vector Machine classification, and k-NN classification are used for determination and safeguard of the diseases.
基于频繁项集挖掘和分类技术的心脏病预测
心脏是人体最重要的部分。心脏的任何异常都会导致心脏相关疾病,这种疾病会阻塞血管,导致心脏病发作、胸痛或中风。通过识别、预防和治疗各种疾病来护理和改善健康是主要目标。因此,我们使用了各种预测分析方法在初步阶段识别疾病从而完成对心脏病的预防和护理。本文强调在心脏疾病的原始阶段的护理,从而导致成功的治愈。本文采用决策树分类、朴素贝叶斯分类、支持向量机分类、k-NN分类等多种数据挖掘分类方法对病害进行判定和防护。
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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