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.
