Heart Disease Detection Using Predictive Optimization Techniques

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...

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Bibliographic Details
Published in:International journal of image, graphics and signal processing graphics and signal processing, 2019-09, Vol.11 (9), p.18-24
Main Authors: Satyanandam, N, Satyanarayana, Ch
Format: Article
Language:English
Subjects:
Algorithms
Analytics
Artificial intelligence
Cardiovascular disease
Data analysis
Data processing
Demand analysis
Digitization
Domains
Health care
Heart
Heart diseases
Machine learning
Optimization
Optimization techniques
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Summary: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.
ISSN:2074-9074
2074-9082
DOI:10.5815/ijigsp.2019.09.02