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A Systematic Review on Prediction Techniques for Cardiac Disease

A Systematic Review on Prediction Techniques for Cardiac Disease

Savita Wadhawan, Raman Maini
Copyright: © 2022 |Volume: 15 |Issue: 1 |Pages: 33
ISSN: 1935-570X|EISSN: 1935-5718|EISBN13: 9781683180289|DOI: 10.4018/IJITSA.290001
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MLA

Wadhawan, Savita, and Raman Maini. "A Systematic Review on Prediction Techniques for Cardiac Disease." IJITSA vol.15, no.1 2022: pp.1-33. http://doi.org/10.4018/IJITSA.290001

APA

Wadhawan, S. & Maini, R. (2022). A Systematic Review on Prediction Techniques for Cardiac Disease. International Journal of Information Technologies and Systems Approach (IJITSA), 15(1), 1-33. http://doi.org/10.4018/IJITSA.290001

Chicago

Wadhawan, Savita, and Raman Maini. "A Systematic Review on Prediction Techniques for Cardiac Disease," International Journal of Information Technologies and Systems Approach (IJITSA) 15, no.1: 1-33. http://doi.org/10.4018/IJITSA.290001

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Abstract

Mortality rate can be lowered with early prediction of cardiac diseases, which is one of the major issue in healthcare industry. In comparison of traditional methods, intelligent systems have potential to predict these diseases accurately at early stage even with complex data. Various intelligent DSS are presented by researchers for predicting this disease. To study the trends of these intelligent systems, to find the effective techniques for predicting cardiac disease and to find the future directions are the objective of this study. Therefore this paper presents a systematic review on state-of-art techniques based on ML, NN and FL. For analysis, we follow PRISMA statement and considered the studies presented from 2010 to 2020 from different databases. Analysis concluded that ML based techniques are broadly used for feature selection and classification and have the potential for the prediction of cardiac diseases. The future directions are to evaluate the rarely used prediction techniques and finding the way of improving them for model generalization with better prediction accuracy.

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