Title: Heart disease patient risk classification based on neutrosophic sets

Authors: Wael K. Hanna; Nouran M. Radwan

Addresses: Computer Science and Information Systems Department, Sadat Academy for Management Science, Cairo, Egypt ' Computer Science and Information Systems Department, Sadat Academy for Management Science, Cairo, Egypt

Abstract: Medical statistics show that heart disease is one of the biggest causes for mortality among the population. In developing countries, people have less concern about their health. The risk is increasing as 500 deaths per 100,000 occur annually in Egypt. The diagnosis of heart disease remains an ambiguous task in the medical field as there are many features involved in order to take the decision. Besides, data gained for diagnosis are often vague and ambiguous. The main contribution of this paper is proposing a novel model of heart disease patient risk classification based on neutrosophic sets. The proposed model is applied to most relevant attributes of selected dataset, and compared to other famous classification techniques such as naive Bayesian, JRip, and random forest for validation. The experimental results indicate that the proposed heart disease classification model achieves highest accuracy and F-measure results in heart disease classification.

Keywords: heart disease; machine learning; classification; neutrosophic sets.

DOI: 10.1504/IJBIDM.2022.119961

International Journal of Business Intelligence and Data Mining, 2022 Vol.20 No.1, pp.93 - 106

Received: 05 Dec 2019
Accepted: 20 Feb 2020

Published online: 17 Dec 2021 *

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