Improving Coronary Artery Disease Prediction
نویسندگان
چکیده
Cardiovascular diseases (CVDs) are the number one cause of death globally. Coronary artery disease (CAD) is most common form CVDs. Abundant research works propose decision support systems for CAD early detection. Most proposed solutions have their origins in realm machine learning and datamining. This paper presents two prediction. The first solution optimizes a random forest model (RFM) through hyperparameters tuning. second uses case-based reasoning (CBR) methodology. CBR takes advantage feature importance to improve execution time retrieve step cycle. experimentations show that RFM outperformed recent published models diagnosis. By reducing attributes, improves also performs very well terms diagnosis accuracy. performance intended be enhanced because
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ژورنال
عنوان ژورنال: International Journal of Decision Support System Technology
سال: 2023
ISSN: ['1941-630X', '1941-6296']
DOI: https://doi.org/10.4018/ijdsst.319307