Medical diagnosis based on single-valued neutrosophic information

Abstract

Women with heart disease during pregnancy are at higher risk, which can harm the fetus. This risk can be reduced if wediagnose and treat it early. The decision-making system is very helpful in such situations. Many clinical decision-making systemshave been proposed, but they are too complicated for medical experts to understand and adapt. Here, we develop a new neutrosophicmodel for early diagnosis and explain it using explainable artificial techniques. Our model is taking eight symptoms and signsas inputs and determines the diagnosis, type of treatment, and prognosis. Age, obesity, smoking, family pathological history,personal pathological history, electrocardiogram, ultrasound, and functional class are the inputs of this model. Six diagnoses canbe made- obstruction at existing, obstruction at entry, rhythm disorder, conduction disorders, congenital diseases, genetic diseases.The types of treatments are- pregnancy interruption, diuretic treatment, anti-arrhythmic treatment, treatment with beta-blockersand anticoagulants treatment. The prognosis is- eutectic delivery, dystocic delivery, the child with complications, child withoutcomplications, mother with complications, and mother without complications. The main parts of this system are neutrosophication,knowledge base, inference engine, de-neutrosophication, and explainability. To present the entire execution of the proposed system,we design an algorithm and compute its time complexity to demonstrate the working of the entire system. We compared the resultsof different methods to gain confidence in our model.