Assessment of Clinical Decision Support Systems for Predicting Coronary Heart Disease

Abstract

The use of data mining approaches in medicine and medical science has become necessary especially with the evolution of these approaches and their contributions medical decision support. Coronary artery disease (CAD) touches millions of people all over the world including a major portion in Algeria. However, much advancement has been done in medical science, but the early detection of CAD is still a challenge for prevention. Although, the early detection of CAD is a prevention challenge for clinicians. The subject of this paper is to propose new clinical decision support system (CDSS) for evaluating risk of CAD called CADSS. In this paper, the authors describe the characteristics of clinical decision support systems CDSSs for the diagnosis of CAD. The aim of this study is to explain the clinical contribution of CDSSs for medical decision-making and compare data mining techniques used for their implementation. Then, they describe their new fuzzy logic-based approach for detecting CAD at an early stage. Rules were extracted using a data mining technique and validated by experts, and the fuzzy expert system was used to handle the uncertainty present in the medical field. This work presents the main risk factors responsible for CAD and presents the designed CASS. The developed CADSS leads to 94.05% of accuracy, and its effectiveness was compared with different CDSS.

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 given approach was implemented in MATLAB. Coronary heart disease prediction system (2016) was developed by Mokeddem et al [ 48 ] hybrid with SIPINA Decision Tree algorithm and Fuzzy logic. The system used 13 input features from the well-known CCF, HIC, LBF and UCI heart disease data sets using triangular, trapezoidal, R and L membership functions.…”

“…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][43][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)…”

Diabetes Diagnosis by Case-Based Reasoning and Fuzzy Logic

“…5 lists a sample about fuzzy rules applied for the Diabetes classification system. The fuzzy rules evaluations and combining results of individual rules are performed by fuzzy set operations [42]. The fuzzy sets operations are different from those on non-fuzzy sets.…”

“…The aggregation result is converted into a crisp value for Diabetes Mellitus output. This is conducted by the defuzzification process [42]. A single number represents the fuzzy set outcome.…”