The in-time and correct diagnosis of coronary artery disease (CAD) assists cardiologists to take the right decisions. The
patient’s gender afects the diagnosis, treatment process, and recovery program. The patient’s gender afects the structure and performance of the CAD diagnosis system. This paper studies the patient’s gender efects on the CAD diagnosis
model structure and performance. The work in the paper built two separate and individual models: male and female.
The feature set of each model was selected using the features ranking voting (FRV) Algorithm. The memberships of the
selected features for each model were computed using the probabilistic clustering technique. We built 38 diferent classifers for each model to select the best one with high performance and a simple structure. The results of each selected
diagnosis model of each gender were analyzed and compared with related works. The comparison shows that the proposed approach outperforms current models and with a simple structure. The accuracy of the male diagnosis model
was 95%, with a sensitivity of 96% and specifcity of 100%. The accuracy of the female diagnosis model was 96%, with a
sensitivity of 97% and specifcity of 96%. The high-performance results prove the success of the proposed gender-based
approach for the diagnosis of coronary artery disease.
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