Prediction of cholesterol level in patients with myocardial infarction based on medical data mining methods

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dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authoridErdil, Nevzat/0000-0002-8275-840X;
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.authorwosidColak, M. Cengiz/ABI-3394-2020
dc.authorwosidErmis, Necip/A-5184-2018
dc.authorwosidErdil, Nevzat/K-8079-2019
dc.authorwosidErmis, Necip/HJP-7061-2023
dc.contributor.authorColak, Cemil
dc.contributor.authorColak, Mehmet C.
dc.contributor.authorErmis, Necip
dc.contributor.authorErdil, Nevzat
dc.contributor.authorOzdemir, Ramazan
dc.date.accessioned2024-08-04T20:42:34Z
dc.date.available2024-08-04T20:42:34Z
dc.date.issued2016
dc.departmentİnönü Üniversitesien_US
dc.description.abstractMyocardial infarction (MI) is a significant reason for death and disability over the world and might be the first sign of coronary artery disease. The current study was carried out to predict the cholesterol level in patients with MI using data mining methods, artificial neural networks (ANNs) and support vector machine (SVM) models. The data of 596 patients, who had been diagnosed with segment elevation MI were analysed in the present study. The retrospective dataset including gender, age, weight, height, pulse, glucose, creatinine, triglyceride, high-density lipoprotein, and low-density lipoprotein was used for predicting the cholesterol level. Correlation based feature selection was applied. Multilayer perceptron (MLP) ANNs and SVM with radial basis function kernel were used for the prediction based on the selected predictors. The performance of the ANNs and SVM models was evaluated on the basis of correlation coefficient and mean absolute error. The estimated correlation coefficients observed and predicted values were 0.94 for ANNs and 0.88 for SVM in training dataset (n=376), and 0.95 for ANNs and 0.90 for SVM in testing dataset (n=160), respectively. ANNs and SVM models yielded mean absolute error of 7.37 and 14.18 in training dataset, and 7.87 and 14.71 in testing dataset, consecutively. The results of the performance evaluation showed that MLP ANNs performed better for the prediction of cholesterol level in patients with MI in comparison to SVM. The proposed MLP ANNs model might be employed for predicting the level of cholesterol for MI patients in clinical decision support process.en_US
dc.identifier.endpage90en_US
dc.identifier.issn2307-4108
dc.identifier.issn2307-4116
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-84981274158en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage86en_US
dc.identifier.urihttps://hdl.handle.net/11616/97453
dc.identifier.volume43en_US
dc.identifier.wosWOS:000386468000010en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofKuwait Journal of Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networks (ANNs)en_US
dc.subjectcholesterol levelen_US
dc.subjectmedical data miningen_US
dc.subjectmyocardial infarction (MI)en_US
dc.subjectsupport vector machine (SVM)en_US
dc.titlePrediction of cholesterol level in patients with myocardial infarction based on medical data mining methodsen_US
dc.typeArticleen_US

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