Application of Medical Data Mining on the Prediction of APACHE II Score

dc.contributor.authorÇolak, Cemil
dc.contributor.authorAydoğan, Mustafa Said
dc.contributor.authorArslan, Ahmet Kadir
dc.contributor.authorYücel, Aytaç
dc.date.accessioned2024-08-04T19:53:22Z
dc.date.available2024-08-04T19:53:22Z
dc.date.issued2015
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe Acute Physiology and Chronic Health Evaluation (APACHE II) is a beneficial tool for the estimation of risk and the comparison of the patients who received care with similar risk properties. Machine learning based systems can assist clinicians in the early diagnosis of diseases. This research aimed at predicting the APACHE II score using Support Vector Machine (SVM) from Medical Data Mining (MDM). The records of 280 patients from intensive care unit included the dataset containing the target variable (the APACHE II score), and 23 demographical/clinical predictor variables. Genetic algorithm based feature selection and 10-fold cross validation method were employed. SVM with radial basis (RBF) was constructed. The performance of the proposed approach was assessed using root mean squared error (RMSE), mean absolute error (MAE), correlation (R) and coefficient of determination (R2). Mean age of the individuals was 51±23 years. 153 (54.6%) were females, and 127 (45.4%) were males. The proposed approach yielded the values of 1.037 for RMSE, 0.727 for MAE, 0.993 for R and 0.986 for R, respectively. The results demonstrated that the proposed approach had an excellent predictive performance of the APACHE II score. Additionally, ensemble approaches such as bagging, boosting, voting etc. can improve markedly the performance of the prediction and classification tasks.en_US
dc.identifier.endpage2750en_US
dc.identifier.issn2147-0634
dc.identifier.issue4en_US
dc.identifier.startpage2743en_US
dc.identifier.trdizinid249485en_US
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/249485
dc.identifier.urihttps://hdl.handle.net/11616/89714
dc.identifier.volume4en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofMedicine Scienceen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleApplication of Medical Data Mining on the Prediction of APACHE II Scoreen_US
dc.typeArticleen_US

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