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    An expert system for the prediction of stroke disease by different least squares support vector machines models
    (Scientific Publishers of India, 2017) Sarihan M.E.; Hanbay D.
    Objective: One of the important life-threatening ailment is stroke across the world. The current paper was performed to classify the outcome of stroke by using Least-Squares Support Vector Machines (LSSVMs) models. Materials and methods: The medical dataset related to stroke disease was achieved from the clinical database of the emergency medicine department. 28 predictors were recorded in raw dataset. For dimension reduction, correlations between input and target (stroke) variables were evaluated. Different LS-SVMs models were performed with radial basis function (RBF), linear and polynomial kernels. 5- fold cross-validation was used in composing stages to achieve the best model using all of the data. The accuracy and the Area under Receiver Operating Curve (AUC ROC) values were used for performance assessment. Results: At first, feature selection stage was performed. 14 input variables were determined after this stage. Whole dataset was partitioned into 5 sub-datasets (D1,D2, D3, D4, D5) to use all data both training and testing. LS-SVMs models performance were evaluated by using 5-fold cross validation method. Accuracy and AUC values of the models were used as performance criteria. The best model performance was evaluated with LS-SVMs model using linear kernel. That model average accuracy was 86.6%. The best accuracy was evaluated with LS-SVM model using linear kernel on dataset D5 was 94%. As a consequence, the LS-SVMs model can be used for predicting the outcome of stroke. Conclusion: The results point out that LS-SVMs with linear kernel have much more accuracy and AUC values for predicting stroke disease. The suggested LS-SVMs with linear kernel may produce beneficial prediction results related to stroke disease. In future studies, several data mining techniques may be tested and assembled for better classification performance of stroke disease. © 2017, Scientific Publishers of India, All rights reserved.
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    Falls from height: A retrospective analysis
    (Second Affiliated Hospital, Zhejiang University School of Medicine, 2021) Turgut K.; Sarihan M.E.; Colak C.; Güven T.; Gür A.; Gürbüz S.
    BACKGROUND: Emergency services manage trauma patients frequently and falls from height comprise the main cause of emergency service admissions. In this study, we aimed to analyse the demographic characteristics of falls from height and their relationship to the mortality. METHODS: A total of 460 patients, who admitted to the Emergency Department of Inonu University between November 2011 and November 2014 with a history of fall from height, were examined retrospectively. Demographic parameters, fall characteristics and their effect to mortality were evaluated statistically. RESULTS: The study comprised of 292 (63.5%) men and 168 (36.5%) women patients. The mean age of all patients was 27±24.99 years. Twenty-six (5.6%) patients died and the majority of them were in ?62 years old group. The highest percentage of falls was at 0–5 years age group (28.3%). People fell mainly from 1.1–4 metres(m) level (46.1%). The causes of falls were ordered as unintentional (92.2%), workplace (8.1%) and suicidal (1.7%). Skin and soft tissue injuries (37.4%) were the main traumatic lesions. CONCLUSION: Age, fall height, fall place, linear skull fracture, subarachnoidal hemorrhage, cervical fracture, thoracic vertebra fracture and trauma scores had statistically significant effect on mortality. The casualties died because of subarachnoid hemorrhage mostly. © 2018 World Journal of Emergency Medicine