A Neutrosophic Intelligent System for Heart Disease Diagnosis

Abstract

Heart disease diagnosis depends on vague, imprecise, ambiguity and inconsistent combination of clinical and pathological data. Therefore, researches in these fields tend to the use of intelligent systems to overcome the uncertainty found in data. This paper suggests neutrosophic logic to obtain a better decision of heart diagnosis with the desire to reduce the number of tests required to be taken on a patient and solve the information uncertainty issue. This paper analyses the dataset to extract the five common features that affect heart disease in Egypt, which are blood pressure, blood sugar, cholesterol, chest pain, and maximum heart rate. Then; it presents a neutrosophic diagnosing system for heart disease depends on a dataset from Egyptian persons were used and independently verified by three experts using semi-structured questionnaire. Finally, the comparison results between human experts, and the presented neutrosophic diagnosing system shows an accuracy of 87% of the proposed system compared with 73% of the fuzzy system.