A survey on risk assessments of heart attack using data mining approaches

A survey on risk assessments of heart attack using data mining approaches

Автор: Yogita Solanki, Sanjiv Sharma

Журнал: International Journal of Information Engineering and Electronic Business @ijieeb

Статья в выпуске: 4 vol.11, 2019 года.

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This document presents the required layout of articles to Medical data mining has become one of the prominent issues in the field of data mining due to the delicate lifestyle opted by the people which are leading them towards various chronicle health diseases. Heart disease is one of the conspicuous public health concern worldwide issues. Since clinical data is growing rapidly owing to deficient health awareness, various techniques and scientific methods are opted for analyzing this huge data. Several data mining techniques such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision tree, Naïve Bayes and Artificial Neural Network (ANN) are introduced for the prediction of health disease. These techniques help to mine the relevant and useful amount of data, form the medical dataset which helps to provide beneficial information to the medical institutions. This study presents various issues related to healthcare and various machines learning algorithms which have to withstand to provide the best possible output. A comprehensive review of the literature has been summarized to put lights on the previous work done in this field.

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Medical Data Mining, Machine Learning algorithm, Heart Disease Prediction, Heart Disease, Comprehensive Review

Короткий адрес: https://sciup.org/15016183

IDR: 15016183   |   DOI: 10.5815/ijieeb.2019.04.05

Список литературы A survey on risk assessments of heart attack using data mining approaches

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