Fuzzy based expert system for diagnosis of coronary artery disease in nigeria

Abstract

Expert system is an artificial intelligence based system that imitates the decision making ability of human and it is used as the diagnostic tool for many diseases including diabetes mellitus, COVID-19, cancers, coronary artery disease (CAD), among other diseases. Even though CAD is globally one of the deadliest diseases and it is not well known in Nigeria, it causes many deaths as such in 2014, 53,836 or 2.82% of total deaths in Nigeria resulted from the CAD. In this study, fuzzy based expert system for diagnosis of CAD is developed in order to provide the complementary diagnostic tools for diagnosis of CAD’s patients in Nigeria. The improved C4.5 data mining algorithm is used to transfer the knowledge of human expert to the knowledge base on the expert system instead of using conventional techniques such as interviews, questionnaires, etc. Taken together, the performance evaluation system was carried out, and the system has an overall accuracy, sensitivity and specificity of 94.55%, 95.35% and 95.00% respectively; which show that, the system is reliable and capable of diagnosing both negative and positive cases of CAD patients efficiently.

Machine Learning Predictive Models for Coronary Artery Disease

“…Gradient boosting is a supervised machine learning algorithm for regression and classification tasks and builds a prediction model in a form of an ensemble of weak prediction model, usually in decision tree form [ 10 , 11 ]. A weaker decision is usually called gradient boosted tree, which often outperforms random forest.…”

Fuzzy Logic and Hybrid based Approaches for the Risk of Heart Disease Detection: State-of-the-Art Review

“…of publications of SCI index Publisher Research paper No. of researches IEEE [ 5 – 25 ] 21 Springer [ 26 – 38 ] 13 Elsevier [ 39 – 47 ] 9 ACM [ 3 , 48 , 49 , 51 ] 4 …”

“…The combination of two or more than two technologies to find the solution of the peculiar problem, overcome the deficiency of one technique and strengthen the system are known as hybrid systems. In this study, hybridization of techniques has been described as, FL [ 8 , 9 , 11 , 12 , 14 , 15 , 19 , 20 , 25 – 32 , 35 , 36 , 38 – 41 , 45 , 48 , 50 , 52 , 53 ], GA [ 5 , 7 , 10 , 14 , 18 , 26 , 27 , 36 , 44 ], Artificial Neural Network (ANN) [ 5 , 7 , 10 , 13 , 35 , 42 – 44 , 51 ], Particle Swarm Optimization (PSO) [ 13 , 33 , 41 ], Ant Colony Optimization (ACO) [ 37 ], Naive Bayes (NB) [ 16 , 18 , 23 , 29 , 34 ], Decision Tree (DT) [ 8 , 18 , 20 , 22 , 23 , 28 , 41 , 48 , 51 ], K-means Clustering Algorithm [ 22 ], Logistic Regression (LR) [ 23 , 42 ], Multivariate Adaptive Regression Splines (MARS) [ 42 ], Rough Set (RS) [ 3 , 42 ], Support Vector Machine (SVM) [ 33 , 34 ], Random Forest (RF) [ 21 , 23 ], K-Nearest Neighbourhood (K-NN) […”

“…Classification of heart disease was done in Weka software tool using the data of 227 patients obtained from CCF and StaLog heart disease data set. A new and unique hybrid technique (2021) using Fuzzy Logic along with Improved C4.5 algorithm was designed and implemented by Muhammad et al [ 38 ]. The system consisted of 11 input variables, namely AG, RBP, glucose, LDL CHOL, HDL CHOL, triglycerides, BCL, BMI, MHR, CP and CHL.…”

“…All the papers come up with distinct techniques to improve the accuracy in the diagnosis system of heart. The highest accuracy (99.4%) is achieved by using Fuzzy Logic hybrid with Improved C4.5 algorithm in the current year 2021 [ 38 ]. Existence of uncertainty is always there in the detection of any disease.…”