A neutrosophic clinical decision-making system for cardiovascular diseases risk analysis

Article type: Research Article

Authors: Habib, Shaista^{a; *} | us Salam, Wardat^b | Butt, M. Arif^c | Akram, M.^d | Smarandache, F.^e

Affiliations: [a] School of Systems and Technology, University of Management and Technology, Lahore, Pakistan | [b] Department of Mathematics, Division of Science & Technology, University of Education, Lahore, Pakistan | [c] Punjab University College of Information Technology, University of the Punjab, Lahore, Pakistan | [d] Department of Mathematics, University of the Punjab, New Campus, Lahore, Pakistan | [e] Mathematics and Science Department, University of New Mexico, Gallup, NM, USA

Correspondence: [*] Corresponding author. Shaista Habib, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan. E-mail: shaista.habib@umt.edu.pk.

Abstract: Cardiovascular diseases are the leading cause of death worldwide. Early diagnosis of heart disease can reduce this large number of deaths so that treatment can be carried out. Many decision-making systems have been developed, but they are too complex for medical professionals. To target these objectives, we develop an explainable neutrosophic clinical decision-making system for the timely diagnose of cardiovascular disease risk. We make our system transparent and easy to understand with the help of explainable artificial intelligence techniques so that medical professionals can easily adopt this system. Our system is taking thirty-five symptoms as input parameters, which are, gender, age, genetic disposition, smoking, blood pressure, cholesterol, diabetes, body mass index, depression, unhealthy diet, metabolic disorder, physical inactivity, pre-eclampsia, rheumatoid arthritis, coffee consumption, pregnancy, rubella, drugs, tobacco, alcohol, heart defect, previous surgery/injury, thyroid, sleep apnea, atrial fibrillation, heart history, infection, homocysteine level, pericardial cysts, marfan syndrome, syphilis, inflammation, clots, cancer, and electrolyte imbalance and finds out the risk of coronary artery disease, cardiomyopathy, congenital heart disease, heart attack, heart arrhythmia, peripheral artery disease, aortic disease, pericardial disease, deep vein thrombosis, heart valve disease, and heart failure. There are five main modules of the system, which are neutrosophication, knowledge base, inference engine, de-neutrosophication, and explainability. To demonstrate the complete working of our system, we design an algorithm and calculates its time complexity. We also present a new de-neutrosophication formula, and give comparison of our the results with existing methods.

Keywords: Single-valued neutrosophic number, explainable artificial intelligence, cardiovascular diseases, decision making, de-neutrosophication, algorithm

DOI: 10.3233/JIFS-201163

Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7807-7829, 2020