title: Different medical data mining approaches based prediction of ischemic stroke.
abstract: OBJECTIVE
  Medical data mining (also called knowledge discovery process in medicine) processes for extracting patterns from large datasets. In the current study, we intend to assess different medical data mining approaches to predict ischemic stroke.
  
  
  METHODS
  The collected dataset from Turgut Ozal Medical Centre, Inonu University, Malatya, Turkey, comprised the medical records of 80 patients and 112 healthy individuals with 17 predictors and a target variable. As data mining approaches, support vector machine (SVM), stochastic gradient boosting (SGB) and penalized logistic regression (PLR) were employed. 10-fold cross validation resampling method was utilized, and model performance evaluation metrics were accuracy, area under ROC curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. The grid search method was used for optimizing tuning parameters of the models.
  
  
  RESULTS
  The accuracy values with 95% CI were 0.9789 (0.9470-0.9942) for SVM, 0.9737 (0.9397-0.9914) for SGB and 0.8947 (0.8421-0.9345) for PLR. The AUC values with 95% CI were 0.9783 (0.9569-0.9997) for SVM, 0.9757 (0.9543-0.9970) for SGB and 0.8953 (0.8510-0.9396) for PLR.
  
  
  CONCLUSIONS
  The results of the current study demonstrated that the SVM produced the best predictive performance compared to the other models according to the majority of evaluation metrics. SVM and SGB models explained in the current study could yield remarkable predictive performance in the classification of ischemic stroke.
authors.affiliation: Inonu University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, Malatya, Turkey. Electronic address: ahmetkadirarslan@gmail.com.
authors.forename: Ahmet Kadir
authors.initials: AK
authors.lastname: Arslan
authors.affiliation: Inonu University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, Malatya, Turkey.
authors.forename: Cemil
authors.initials: C
authors.lastname: Colak
authors.affiliation: Inonu University, Faculty of Medicine, Department of Emergency Medicine, Malatya, Turkey.
authors.forename: Mehmet Ediz
authors.initials: ME
authors.lastname: Sarihan
mesh_terms: D057225:Data Mining; D006801:Humans; D020521:Stroke / Q000473:pathology*; D060388:Support Vector Machine; D014421:Turkey
keywords: Ischemic stroke; Medical data mining; Penalized logistic regression; Stochastic gradient boosting; Support vector machine