使用机器学习和Pearson的心脏病发作决策系统

2017 International conference of Electronics, Communication and Aerospace Technology (ICECA) Pub Date : 2017-04-01 DOI:10.1109/ICECA.2017.8212797

Chandrasegar Thirumalai, Anudeep Duba, Rajasekhar Reddy

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引用次数: 42

摘要

这些信息收集被用来预测病人心脏病发作的几率。在尖端智能手机的季节贡献12个属性是不可行的。我们对给定的信息集合进行产品度量检验。鉴于对信息的调查，我们试图将属性的总数缩小到一个小数字，最后，我们可以选择哪些属性可以考虑，哪些特征可以忽略。

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Decision making system using machine learning and Pearson for heart attack

This informational collection is utilized to anticipate the odds of an event of heart assault for a patient. In the season of cutting edge smartphones contributing 12 attributes is not feasible. We play out the product metric examination on the given informational collection. In view of the investigation of information we try to bring the total number of attributes into a small figure and in the end, we may be able to choose which property can be considered and which characteristic can be disregarded.

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2017 International conference of Electronics, Communication and Aerospace Technology (ICECA)

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