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.
Highlights
Artificial intelligence (AI) is an umbrella term for algorithms aiming at delivering task of solving capabilities comparable to humans
According to Pearl et al, there are three Layers of causal hierarchy to measure the quality of explanation [34]-[35]: Level 1: Association- How much mentioned risk factors belongs to the specified cardiovascular disease? This question is asked by medical experts and concluded that all the mentioned risk factors are closely related to cardiovascular diseases (CVDs)
Our system helps medical experts to early detect the risk of CVDs so that precautionary measures can be taken timely
Summary
Artificial intelligence (AI) is an umbrella term for algorithms aiming at delivering task of solving capabilities comparable to humans. Immediate probabilities aggregation operators for single-valued and interval neutrosophic sets is another method of decision making which describes the decision-maker’s behavior objectively (in terms of probability) and subjectively (in terms of weight), with the concept of probabilistic information playing a dominant role in the investigation The advantage of these proposed operators is that they simultaneously combine objective and subjective behavior in the decision-making process [16]. In 2016, Ye et al [23] proposed a novel single-valued neutrosophic similarity measuring method to resolve multi-period medical diagnosis problems This technique used the tangent function and weighted aggregation of multi-period data. A new parametric divergence measure for neutrosophic sets has suggested along with its various characteristics Based on this method parametric divergence measured and outlined some methodologies along with its implementing procedural steps for classification problems and multi-criteria decision-making problem.
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