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NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease
journal contribution
posted on 2019-01-01, 00:00 authored by Moloud Abdar, U R Acharya, N Sarrafzadegan, V Makarenkov© 2013 IEEE. Coronary artery disease (CAD) is one of the main causes of cardiac death around the world. Due to its significant impact on the society, early and accurate detection of CAD is essential. This study proposes a novel nested ensemble nu-Support Vector Classification (NE-nu-SVC) model which combines several traditional machine learning methods and ensemble learning techniques for effective diagnosis of CAD. We validated our model using two well-known CAD datasets (Z-Alizadeh Sani and Cleveland). To improve the performance of the model, we selected clinically significant features from the datasets using a genetic search algorithm. To further improve our results, we applied a multi-level filtering technique to balance the data using the ClassBlancer and Resample methods. Our base algorithm, nu-SVC, is performed using four well-known kernel functions (linear, polynomial, radial basis (RBF) and sigmoid). The proposed NE-nu-SVC model provided the highest accuracy of 94.66% and 98.60% to predict CAD entities in the Z-Alizadeh Sani and Cleveland CAD datasets, respectively. Our system can aid the clinicians to diagnose CAD accurately and may probably replace other invasive diagnostic techniques.
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Journal
IEEE AccessVolume
7Pagination
167605 - 167620Publisher
IEEELocation
Piscataway, N.J.Publisher DOI
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eISSN
2169-3536Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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Coronary artery disease (CAD)machine learningensemble learningnested ensemble (NE) modelgenetic algorithmScience & TechnologyTechnologyComputer Science, Information SystemsEngineering, Electrical & ElectronicTelecommunicationsComputer ScienceEngineeringIMMUNE RECOGNITION SYSTEMDATA MINING TECHNIQUESHEART-DISEASEECG SIGNALSNEURAL-NETWORKSCLASSIFICATIONPREDICTIONPERFORMANCEALGORITHMMODEL
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