SAMPLE SIZE EFFECT ON CLASSIFICATION PERFORMANCE OF MACHINE LEARNING MODELS: AN APPLICATION OF CORONARY ARTERY DISEASE

Year 2020, Volume: 5 Issue: 1, 15 - 18, 30.06.2020

Mehmet Kıvrak , Faruk Berat Akçeşme , Cemil Çolak

Abstract

Cardiovascular diseases are among the most common causes of death due to their widespread prevalence. Accurate and timely diagnosis of coronary artery disease, one of the fatal cardiovascular diseases, is very important. Angiography, an invasive method, is an expensive and special method used to determine the disease and can cause serious complications. Therefore, cheaper and more efficient data mining methods are used in the diagnosis and treatment of cardiovascular diseases. As an alternative approach, by establishing clinical decision support systems using data modeling and analysis methods such as data mining, errors and costs can be reduced by providing clinicians with computer-aided diagnosis, and patient safety and clinical decision quality can be significantly increased. In this study, the data set on the open-source access website was used to classify cardiovascular disease and consists of patient records of 14 variables created by the Cleveland clinic. Also, machine learning methods (C5.0 Decision Tree, Support Vector Machine, Multilayer Perceptron, and Ensemble Learning)were used to determine the risk of coronary artery disease by deriving 1000 and 10000 data sets from the cardiology data set obtained from original 303 patient records. Performance evaluation of models is compared in terms of accuracy, specificity, and sensitivity. In trying to determine the most successful model in estimating the risk of coronary artery disease, the results are presented comparatively.

Keywords

Cardiovascular Diseases, Sample Size, Data Mining, Ensemble Learning

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