Different medical data mining approaches based prediction of ischemic stroke. Issue 130 (July 2016)

• Record Type: Journal Article
• Authors: Arslan, Ahmet Kadir; Colak, Cemil; Sarihan, Mehmet Ediz
• Is Part Of: Computer methods and programs in biomedicine. Issue 130(2016)
• Page Start: 87
• Page End: 92
• Publication Date: 2016-07
• Subjects: Ischemic stroke -- Medical data mining -- Penalized logistic regression -- Stochastic gradient boosting -- Support vector machine; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.03.022
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We assessed different medical data mining approaches to predict ischemic stroke. Grid search were used for improving classification performance of the models. The accuracy and AUC values were higher than 0.8947 and 0.8953, respectively. SVM and SGB models yielded remarkable performance for classifying ischemic stroke. Abstract: Aim: 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. Materials and 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.