A Novel Dyno-Quick Reduct Algorithm for Heart Disease Prediction Using Supervised Learning Algorithm

T. Marikani and K. Shyamala
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T. Marikani: Sree Muthukumaraswamy College, Department of Computer Science
K. Shyamala: Dr. Ambedkar Govt. Arts College (Autonomous), P.G. and Research Department of Computer Science

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 267-274 from Springer

Abstract: Abstract Diagnosing of heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. Data classification can often be applied to medical data helps to detect the prevalence of disease. Many tools and algorithms are proposed by researchers to develop an effective medical decision system. Feature selection refers to the problem of Selecting relevant features to produce the most predictive outcome is called as feature selection. This paper proposes a new feature selection method based on rough set theory with modified dynamic quick reduct algorithm.

Keywords: Data Mining; Rough set theory; Feature selection; Dynamic quick reduction (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_24

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DOI: 10.1007/978-3-030-41862-5_24

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