Abstract：
INTRODUCTION:Coronary artery disease (CAD) is still one of the primary causes of death in the developed countries. Stress single-photon emission computed tomography is used to evaluate myocardial perfusion and ventricular function in patients with suspected or known CAD. This study sought to test data mining and machine learning tools and to compare some supervised learning algorithms in a large cohort of Italian subjects with suspected or known CAD who underwent stress myocardial perfusion imaging. METHODS:The dataset consisted of 10,265 patients with suspected or known CAD. The analysis was conducted using Knime analytics platform in order to implement Random Forests, C4.5, Gradient boosted tree, Naïve Bayes, and K nearest neighbor (KNN) after a procedure of features filtering. K-fold cross-validation was employed. RESULTS:Accuracy, error, precision, recall, and specificity were computed through the above-mentioned algorithms. Random Forests and gradients boosted trees obtained the highest accuracy (>95%), while it was comprised between 83% and 88%. The highest value for sensitivity and specificity was obtained by C4.5 (99.3%) and by Gradient boosted tree (96.9%). Naïve Bayes had the lowest precision (70.9%) and specificity (72.0%), KNN the lowest recall and sensitivity (79.2%). CONCLUSIONS:The high scores obtained by the implementation of the algorithms suggests health facilities consider the idea of including services of advanced data analysis to help clinicians in decision-making. Similar applications of this kind of study in other contexts could support this idea.
journal_name
Comput Methods Programs Biomedjournal_title
Computer methods and programs in biomedicineauthors
Ricciardi C,Cantoni V,Improta G,Iuppariello L,Latessa I,Cesarelli M,Triassi M,Cuocolo Adoi
10.1016/j.cmpb.2020.105343subject
Has Abstractpub_date
2020-06-01 00:00:00pages
105343eissn
0169-2607issn
1872-7565pii
S0169-2607(19)30341-4journal_volume
189pub_type
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journal_title：Computer methods and programs in biomedicine
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