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Öğe The association of ABO blood group and rh factor with recurrent aphthous ulceration(2018) Sagiroglu, Saime; Oztarakci, Huseyin; Ozturk, Perihan; Doganer, Adem; Koca, Tuba Tulay; Bilal, Nagihan; Sarica, Selman; Orhan, IsrafilAim: In this study we aimed to investigate effects of blood group and Rh factor on recurrent aphthous ulceration (RAS). Material and Methods: A total of 350 persons were included in the study, 175 with RAS and 175 as the healthy control group. Medical histories and laboratory findings of the patients presenting to the outpatient clinic were evaluated. Patients that had aphthae lesions more than three times a year were studied. Haemoglobin (Hb), vitamin B12, ferritin, folic acid, and iron levels were measured and the blood groups were recorded. Results: Of RAS patients, 16.8% had a deficiency in Hb, 16.3% in vitamin B12, 18.5% in ferritin, 6.4% in folic acid and 28.2% in iron. The patient blood groups were distributed as follows 33.7% Group A, 20% Group B, 8.6% Group AB and 33.1% Group O. Of RAS patients were 92% Rh(+) and 8% Rh(-). No statistically significant difference was found between the distribution of blood groups and RAS. However, the risk of RAS was found to be six times higher in B Rh(+) patients compared to B Rh(-) patients and three times higher in AB Rh(+) patients com-pared to AB Rh(-) patients. Conclusions: Rh factor may have an effect on the etiology of RAS disease. Anemia and vitamin B12 deficiency are common in RAS patients, making a hematological evaluation a necessity for RAS patientsÖğe Different machine learning methods based prediction of mild cognitive impairment(2020) Doganer, Adem; Yaman, Selma; Eser, Nadire; Ozcan Metin, TugbaAim: In this study benefits from different machine learning methods to analyze factors which affect young person’s scores of cognitive assessment. Material and Methods: This study was performed among 144 persons aged between 18 and 24 who study at Kahramanmaras Sutcu Imam University. Boosted Tree Regression (BTR), Random Forest Regression (RFR) and Support Vector Machine (SVM), which are among machine learning methods, were used in order to determine the factors affecting the score of cognitive assessment. K-10 fold cross validation method was also used. Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Correlation coefficients (R) metrics were used in order to measure prediction performances of machine learning methods.Results: MSE values were calculated as 9.66 for BTR, 9.78 for RFR, and 6.43 for SVM. MAE values were calculated as 2.06 for BTR, 2.05 for RFR, and 1.97 for SVM. RMSE values were calculated as 3.10 for BTR, 3.12 for RFR, and 2.53 for SVM. Finally, correlation coefficients were calculated as 0.289 for BTR, 0.371 for RFR and 0.546 for SVM. In addition, it was also found out that the most important variables which affected the scores of cognitive assessment were anti-depressant use, depression and obsession.Conclusion: It was demonstrated in this study that SVM displayed the lowest error rates and highest prediction performance in terms of determining the score of cognitive assessment. Therefore, SVM can be stated that it is the most suitable method for the prediction of cognitive impairment.Öğe Ensemble learning-based prediction of COVID-19 positive patient groups determined by IL-6 levels and control individuals based on the proteomics data(2021) Yasar, Seyma; Kucukakcali, Zeynep; Doganer, AdemCoronavirus disease (COVID-19) is a newly found coronavirus that causes an infectious disease. COVID-19, which has a detrimental impact on many people, has varied effects on different people. Therefore, proteomic analysis is an important approach used to develop early diagnosis and treatment strategies. This research to classify COVID-19 positive patient groups represented by interleukin 6 (IL-6) levels (low, medium, high) and control groups based on proteomic analysis using ensemble learning methods (Adaboost, Bagging, Stacking, and Voting). The public dataset from a website consists of 49 subjects (31 COVID-19 positives and 18 controls) and 493 proteins achieved from blood samples. The dataset was handled to estimate the relation between disease severity and proteins using ensemble learning approaches (Adaboost, Bagging, Stacking, and Voting) using ten-fold cross-validation. Predictions were evaluated with accuracy, sensitivity,etc. performance metrics. The accuracy of Adaboost (96.00%) was higher as compared to Voting (93.88%) and Bagging (91.84%). However, the Stacking ensemble learning method produced the highest accuracy (97.92%). IL6, SERPINA3, SERPING1, SERPINA1, and GSN were the five most important proteins associated with disease severity. In comparison to the other methods, the suggested ensemble learning model (Stacking) produced the best estimation of disease severity based on proteins. The results indicate that changes in blood protein levels correlated with the severity of COVID-19 may be benefited to follow early diagnosis/treatment of the COVID-19 disease.Öğe Knowledge, attitudes and behaviors of medical faculty students about family medicine(2020) Kala, Eyyup; Gumustakim, Raziye Sule; Doganer, Adem; Kus, CelalAim: It was aimed to determine the knowledge levels of medical faculty studentsabout the family medicine discipline, their approach to primary care, their interest in the family medicine career and the factors affecting it. Material and Methods: The universe of our study was 713 people, and the sample size was calculated as 250 people in 5% margin of error and 95% confidence interval. A questionnaire included 62 questions which were aboutthe knowledge, attitudes and behaviors of medical students about family medicine.In the variables with normal distribution, the comparisons of the two groups were carried out with two independent samples t test.In non-normally distributed variables, comparisons of the two groups were examined by Mann-Whitney test.Categorical variables were examined with Chi-Square test. Statistical significance was accepted as p <0.05.The data were evaluated in IBM SPSS version 22 program. Results: 50.4% of the participants were male, 49.5% were female and the mean age was 22.00 ± 2.46. The rate of those who had never been to a family physician in the last year was 41.1%. 28.5% of theparticipants stated that they mostly applied tothe family physician except to the emergencies and the most common reason for going to the family physician (63.8%) was new health problems. The majority of the participants (79.3%) wanted the referral system to be implemented. When the answers of the participants about the duties of family physicians were examined, the three most common known propositions were : ”Family physician vaccinates infancy, childhood and adulthood” (90.2%), ”In the practice of family medicine, dressing, injection and wound care services are provided” (%87.0), and “Family physician can give health report”(%84.1). Conclusions: The majority of medical faculty students apply to secondary or tertiary health care providers when they experience any health problems. The fact that almost half of the participants stated that they have never been to be a family physician in the last year shows numerically how grave the situation is. First of all, people should be informed to adopt family health centers as the first health institution to be consulted
