文章基本信息

标题：Heart Disease Detection Using Predictive Optimization Techniques
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作者：N Satyanandam ; Ch Satyanarayana
期刊名称：International Journal of Image, Graphics and Signal Processing
印刷版ISSN：2074-9074
电子版ISSN：2074-9082
出版年度：2019
卷号：11
期号：9
页码：18-24
DOI：10.5815/ijigsp.2019.09.02
出版社：MECS Publisher
摘要：Health care is a major research domain needed instantaneous solutions. Due to the digitalization of data in each and every domain it is becoming tedious to store and analysis. So, the demand of proficient algorithms for health care data analysis is also increasing. Predictive analytics is the major demand from the health care community to the computing researches in order to predict and reduce the potential health catastrophes. Parallel research attempts are made to predict the possibilities of the disease on the different health care domains at various regions. However, those attempts are limited and not remarkable to achieve the desired outcomes. Recently, in the field of data analytics; Machine Learning techniques became popular in generating optimized solutions with effective data processing capabilities. Henceforth, this research work considers the heart disease analysis using machine learning techniques to determine the disease severity levels. Experiments are made on UCI heart disease dataset and our results shows 92% accuracy the heart severity detection.
关键词：Heart Disease Analysis;Prediction;Optimised Solutions;Machine Learning Techniques;Severity Detection