Branch Mathematics and Statistics Faculty and Staff Publications
Document Type
Article
Publication Date
8-2020
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 thirtyfive 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.
Publication Title
JIFS
First Page
1
Last Page
31
Language (ISO)
English
Keywords
Single-valued neutrosophic number, explainable artificial intelligence, cardiovascular diseases, decision making, de-neutrosophication, algorithm
Recommended Citation
Smarandache, Florentin; Shaista Habib; Wardat-us- Salam; M. Arif Butt; and Muhammad Akram. "A Neutrosophic Clinical Decision-Making System for Cardiovascular Diseases Risk Analysis." JIFS (2020): 1-31. https://digitalrepository.unm.edu/math_fsp/459
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Included in
Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons, Bioinformatics Commons, Health Information Technology Commons, Mathematics Commons, Medical Sciences Commons, Other Life Sciences Commons, Other Medicine and Health Sciences Commons
