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Öğe Assessment of COVID-19-Related Genes Through Associative Classification Techniques(Duzce Univ, Fac Medicine, 2022) Cicek, Ipek Balikci; Kaya, Mehmet Onur; Colak, CemilObjective: This study aims to classify COVID-19 by applying the associative classification method on the gene data set consisting of open access COVID-19 negative and positive patients and revealing the disease relationship with these genes by identifying the genes that cause COVID-19. Methods: In the study, an associative classification model was applied to the gene data set of patients with and without open access COVID-19. In this open-access data set used, 15979 genes are belonging to 234 individuals. Out of 234 people, 141 (60.3%) were COVID-19 negative and 93 (39.7%) were COVID-19 positives. In this study, LASSO, one of the feature selection methods, was performed to choose the relevant predictors. The models' performance was evaluated with accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Results: According to the study findings, the performance metrics from the associative classification model were accuracy of 92.70%, balanced accuracy of 91.80%, the sensitivity of 87.10%, the specificity of 96.50%, the positive predictive value of 94.20%, the negative predictive value of 91.90%, and F1-score of 90.50%. Conclusions: The proposed associative classification model achieved very high performances in classifying COVID-19. The extracted association rules related to the genes can help diagnose and treat the disease.Öğe Assessment of Patient Safety Attitude Levels Among Healthcare Professionals Working in the Operating Room(Erciyes Univ Sch Medicine, 2023) Tamer, Murat; Akbulut, Sami; Cicek, Ipek Balikci; Saritas, Hasan; Akbulut, Mehmet Serdar; Ozer, Ali; Colak, CemilObjective: This study aims to determine the factors affecting the perception levels of operating room (OR) nurses and nurse anesthetists working in the OR regarding patient safety attitudes. Materials and Methods: This study was conducted using face-to-face interviews with 117 healthcare professionals working as OR nurses (n=60) and nurse anesthetists (n=57). The patient safety attitude questionnaire (SAQ), where the reliability analysis was also performed for the SAQ scale. and sociodemographic characteristics were used for this study. Qualitative variables were given as numbers and percentages (%), and the dataset belonging to quantitative variables that met the normal distribution criteria was given as mean (standard deviation), and data of quantitative variables that did not comply with nor-mality were given as median, IQR, and 95% CI of the median.Results: There were significant differences between OR nurses and nurse anesthetists regarding job satisfaction (p=0.015) and total SAQ score (p=0.040). Significant differences were detected between men and women participants regarding smoking (p=0.020) and stress recognition (p=0.040). The reliability analysis of the scale was as follows: total (alpha: 0.791), job satisfaction (alpha: 0.883), teamwork climate (alpha: 0.856), safety climate (alpha: 0.864), perceptions of management (alpha: 0.881), stress recognition (alpha: 0.791), and working conditions (alpha: 0.530).Conclusion: It was shown that the patient safety attitudes of the healthcare professionals participating in this study are above average, although it is still insufficient, where the stress identification score of the female participant was higher, and it was also found that the nurses' job satisfaction and SAQ score were higher.Öğe Classification of Breast Cancer on the Strength of Potential Risk Factors with Boosting Models: A Public Health Informatics Application(Galenos Publ House, 2022) Akbulut, Sami; Cicek, Ipek Balikci; Colak, CemilAim: The diagnosis of breast cancer can be accomplished using an algorithm or an early detection model of breast cancer risk via determining factors. In the present study, gradient boosting machines (GBM), extreme gradient boosting (XGBoost) and light gradient boosting (LightGBM) models were applied and their performances were compared. Methods: The open-access Breast Cancer Wisconsin Dataset, which includes 10 features of breast tumors and results from 569 patients, was used for this study. The GBM, XGBoost, and LightGBM models for classifying breast cancer were established by a repeated stratified K-fold cross validation method. The performance of the model was evaluated with accuracy, recall, precision, and area under the curve (AUC). Results: Accuracy, recall, AUC, and precision values obtained from the GBM, XGBoost, and LightGBM models were as follows: (93.9%, 93.5%, 0.984, 93.8%), (94.6%, 94%, 0.985, 94.6%), and (95.3%, 94.8%, 0.987, 95.5%), respectively. According to these results, the best performance metrics were obtained from the LightGBM model. When the effects of the variables in the dataset on breast cancer were assessed in this study, the five most significant factors for the LightGBM model were the mean of concave points, texture mean, concavity mean, radius mean, and perimeter mean, respectively. Conclusion: According to the findings obtained from the study, the LightGBM model gave more successful predictions for breast cancer classification compared with other models. Unlike similar studies examining the same dataset, this study presented variable significance for breast cancer-related variables. Applying the LightGBM approach in the medical field can help doctors make a quick and precise diagnosis.Öğe Classification of stroke with gradient boosting tree using smote-based oversampling method(2021) Yağın, Fatma Hilal; Cicek, Ipek Balikci; Tunç, ZeynepThe aim of this study is to classify the disease with the gradient increasing tree classification method in an open access dataset containing data from patients with and without stroke disease. In addition, it is aimed to compare the results by balancing the data with the oversampling method Synthetic Minority Over-sampling Technique (SMOTE) which is one of the data balancing methods in the study. In this study, a dataset containing information about patients with and without stroke disease obtained from the address "https://www.kaggle.com/asaumya/healthcare-problem-prediction-stroke-patients" was used. In the study, SMOTE was used as the data balancing method, and the gradient boosting tree method was used in the modeling. The performance of the model was evaluated by Specificity, sensitivity, accuracy, positive predictive value and negative predictive values. Specificity, sensitivity, accuracy, positive predictive value and negative predictive values were obtained as 0.0887, 0.9772, 0.9339, 0.9544 and 0.1679, respectively, according to the modeling result using the gardient boosting tree method using the original version of the dataset. Specificity, sensitivity, accuracy, positive predictive value and negative predictive values were obtained as 0.0887, 0.9772, 0.9339, 0.9544 and 0.1679, respectively, according to the modeling result using the gardient boosting tree method using the SMOTE applied version of the dataset. When the results obtained from the study were examined, the modeling results made with the SMOTE applied dataset were obtained more consistently and realistically. As a result, it is suggested that researchers use dataset balancing methods to acquire more accurate results whenever they come across an unbalanced dataset problem.Öğe Evaluation of the relationship between nasal septal deviation and development of facial asymmetry with anthropometric measurements depending on age(Elsevier Ireland Ltd, 2022) Arpaci, Muhammed Furkan; Ozbag, Davut; Aydin, Sukru; Senol, Deniz; Baykara, Rabia Aydogan; Cicek, Ipek BalikciAim: It was aimed to determine the change of facial asymmetry resulting from nasal septal deviation (SD) depending on age, gender, degree of deviation and the affected area besides the effect of SD on somatotype and craniofacial morphology. Materials and methods: 171 volunteers (90 males, 81 females), 27 individuals aged 9-13, 44 individuals aged 14-18, 44 individuals aged 19-23 and 56 individuals in control group participated in the study conducted in otorhinolaryngology polyclinic.11 photometric, 16 anthropometric measurements were taken from the participants. Results: SD affects facial asymmetry formation, although not statistically significant compared to healthy individuals asymmetry rates (p>0.05). It was determined that the degree of SD affected asymmetry only between the ages of 14-18 (in adolescence) and the development of asymmetry in all SD patients was not statistically dependent on age and gender (p>0.05). Photometric measurements demonstrated asymmetries in horizontally-extending parameters of 1/3 middle part of face. There was no statistically significant difference in the cranial anthropometric measurements of the upper and lower 1/3 of the face compared to the control group (p>0.05). The order of the most asymmetrical parameters is Alare-Zygion, Alare-Subnasale, Cheilion-Gonion, Exocanthion-Cheilion, Midsagittal plane-Zygion, Zygion-Cheilion, Zygion-Gonion, Subalare-Cheilion, Glabella-Exocanthion. In all participants were determined that endomorph somatotype was dominant in female and mesomorph somatotype was dominant in male besides SD did not affect somatotype and somatotype did not alter with age. Conclusion: The development of facial asymmetry due to SD is not affected by age and gender furthermore SD does not affect craniofacial asymmetry and somatotype.Öğe Explainable artificial intelligence model for identifying COVID-19 gene biomarkers(Pergamon-Elsevier Science Ltd, 2023) Yagin, Fatma Hilal; Cicek, Ipek Balikci; Alkhateeb, Abedalrhman; Yagin, Burak; Colak, Cemil; Azzeh, Mohammad; Akbulut, SamiAim: COVID-19 has revealed the need for fast and reliable methods to assist clinicians in diagnosing the disease. This article presents a model that applies explainable artificial intelligence (XAI) methods based on machine learning techniques on COVID-19 metagenomic next-generation sequencing (mNGS) samples.Methods: In the data set used in the study, there are 15,979 gene expressions of 234 patients with COVID-19 negative 141 (60.3%) and COVID-19 positive 93 (39.7%). The least absolute shrinkage and selection operator (LASSO) method was applied to select genes associated with COVID-19. Support Vector Machine -Synthetic Minority Oversampling Technique (SVM-SMOTE) method was used to handle the class imbalance problem. Logistics regression (LR), SVM, random forest (RF), and extreme gradient boosting (XGBoost) methods were constructed to predict COVID-19. An explainable approach based on local interpretable model-agnostic expla-nations (LIME) and SHAPley Additive exPlanations (SHAP) methods was applied to determine COVID-19-associated biomarker candidate genes and improve the final model's interpretability.Results: For the diagnosis of COVID-19, the XGBoost (accuracy: 0.930) model outperformed the RF (accuracy: 0.912), SVM (accuracy: 0.877), and LR (accuracy: 0.912) models. As a result of the SHAP, the three most important genes associated with COVID-19 were IFI27, LGR6, and FAM83A. The results of LIME showed that especially the high level of IFI27 gene expression contributed to increasing the probability of positive class.Conclusions: The proposed model (XGBoost) was able to predict COVID-19 successfully. The results show that machine learning combined with LIME and SHAP can explain the biomarker prediction for COVID-19 and provide clinicians with an intuitive understanding and interpretability of the impact of risk factors in the model.Öğe HBsAg relapse after living donor liver transplantation in hepatocelluler carcinoma patients with hepatitis D virus infection may result in hepatocellular carcinoma relapse(Elsevier, 2020) Baskiran, Adil; Sahin, Tevfik Tolga; Ince, Volkan; Karakas, Serdar; Ozdemir, Fatih; Cicek, Ipek Balikci; Yalcin, Kendal[Abstract Not Available]Öğe Modeling Based on Ensemble Learning Methods for Detection of Diagnostic Biomarkers from LncRNA Data in Rats Treated with Cis-Platinum-Induced Hepatotoxicity(Mdpi, 2023) Kucukakcali, Zeynep; Colak, Cemil; Bag, Harika Gozde Gozukara; Cicek, Ipek Balikci; Ozhan, Onural; Yildiz, Azibe; Danis, NefsunBackground: The first aim of this study is to perform bioinformatic analysis of lncRNAs obtained from liver tissue samples from rats treated with cisplatin hepatotoxicity and without pathology. Another aim is to identify possible biomarkers for the diagnosis/early diagnosis of hepatotoxicity by modeling the data obtained from bioinformatics analysis with ensemble learning methods. Methods: In the study, 20 female Sprague-Dawley rats were divided into a control group and a hepatotoxicity group. Liver samples were taken from rats, and transcriptomic and histopathological analyses were performed. The dataset achieved from the transcriptomic analysis was modeled with ensemble learning methods (stacking, bagging, and boosting). Modeling results were evaluated with accuracy (Acc), balanced accuracy (B-Acc), sensitivity (Se), specificity (Sp), positive predictive value (Ppv), negative predictive value (Npv), and F1 score performance metrics. As a result of the modeling, lncRNAs that could be biomarkers were evaluated with variable importance values. Results: According to histopathological and immunohistochemical analyses, a significant increase was observed in the sinusoidal dilatation and Hsp60 immunoreactivity values in the hepatotoxicity group compared to the control group (p < 0.0001). According to the results of the bioinformatics analysis, 589 lncRNAs showed different expressions in the groups. The stacking model had the best classification performance among the applied ensemble learning models. The Acc, B-Acc, Se, Sp, Ppv, Npv, and F1-score values obtained from this model were 90%, 90%, 80%, 100%, 100%, 83.3%, and 88.9%, respectively. lncRNAs with id rna-XR_005492522.1, rna-XR_005492536.1, and rna-XR_005505831.1 with the highest three values according to the variable importance obtained as a result of stacking modeling can be used as predictive biomarker candidates for hepatotoxicity. Conclusions: Among the ensemble algorithms, the stacking technique yielded higher performance results as compared to the bagging and boosting methods on the transcriptomic data. More comprehensive studies can support the possible biomarkers determined due to the research and the decisive results for the diagnosis of drug-induced hepatotoxicity.Öğe Nephrotoxicity Development of a Clinical Decision Support System Based on Tree-Based Machine Learning Methods to Detect Diagnostic Biomarkers from Genomic Data in Methotrexate-Induced Rats(Mdpi, 2023) Cicek, Ipek Balikci; Colak, Cemil; Yologlu, Saim; Kucukakcali, Zeynep; Ozhan, Onural; Taslidere, Elif; Danis, NefsunBackground: The purpose of this study was to carry out the bioinformatic analysis of lncRNA data obtained from the genomic analysis of kidney tissue samples taken from rats with nephrotoxicity induced by methotrexate (MTX) and from rats without pathology and modeling with the tree-based machine learning method. Another aim of the study was to identify potential biomarkers for the diagnosis of nephrotoxicity and to provide a better understanding of the nephrotoxicity formation process by providing the interpretability of the model with explainable artificial intelligence methods as a result of the modeling. Methods: To identify potential indicators of drug-induced nephrotoxicity, 20 female Wistar albino rats were separated into two groups: MTX-treated and the control. Kidney tissue samples were collected from the rats, and genomic, histological, and immunohistochemical analyses were performed. The dataset obtained as a result of genomic analysis was modeled with random forest (RF), a tree-based method. Modeling results were evaluated with sensitivity (Se), specificity (Sp), balanced accuracy (B-Acc), negative predictive value (Npv), accuracy (Acc), positive predictive value (Ppv), and F1-score performance metrics. The local interpretable model-agnostic annotations (LIME) method was used to determine the lncRNAs that could be biomarkers for nephrotoxicity by providing the interpretability of the RF model. Results: The outcomes of the histological and immunohistochemical analyses conducted in the study support the conclusion that MTX use caused kidney injury. According to the results of the bioinformatics analysis, 52 lncRNAs showed different expressions in the groups. As a result of modeling with RF for lncRNAs selected with Boruta variable selection, the B-Acc, Acc, Sp, Se, Npv, Ppv, and F1-score were 88.9%, 90%, 90.9%, 88.9%, 90.9%, 88.9%, and 88.9%, respectively. lncRNAs with id rnaXR_591534.3 rnaXR_005503408.1, rnaXR_005495645.1, rnaXR_001839007.2, rnaXR_005492056.1, and rna_XR_005492522.1. The lncRNAs with the highest variable importance values produced from RF modeling can be used as nephrotoxicity biomarker candidates. Furthermore, according to the LIME results, the high level of lncRNAs with id rnaXR_591534.3 and rnaXR_005503408.1 particularly increased the possibility of nephrotoxicity. Conclusions: With the possible biomarkers resulting from the analyses in this study, it can be ensured that the procedures for the diagnosis of drug-induced nephrotoxicity can be carried out easily, quickly, and effectively.Öğe A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs(Mdpi, 2023) Dayi, Burak; Uzen, Huseyin; Cicek, Ipek Balikci; Duman, Suayip BurakThe study aims to evaluate the diagnostic performance of an artificial intelligence system based on deep learning for the segmentation of occlusal, proximal and cervical caries lesions on panoramic radiographs. The study included 504 anonymous panoramic radiographs obtained from the radiology archive of Inonu University Faculty of Dentistry's Department of Oral and Maxillofacial Radiology from January 2018 to January 2020. This study proposes Dental Caries Detection Network (DCDNet) architecture for dental caries segmentation. The main difference between DCDNet and other segmentation architecture is that the last part of DCDNet contains a Multi-Predicted Output (MPO) structure. In MPO, the final feature map split into three different paths for detecting occlusal, proximal and cervical caries. Extensive experimental analyses were executed to analyze the DCDNet network architecture performance. In these comparison results, while the proposed model achieved an average F1-score of 62.79%, the highest average F1-score of 15.69% was achieved with the state-of-the-art segmentation models. These results show that the proposed artificial intelligence-based model can be one of the indispensable auxiliary tools of dentists in the diagnosis and treatment planning of carious lesions by enabling their detection in different locations with high success.Öğe Prediction of Perforated and Nonperforated Acute Appendicitis Using Machine Learning-Based Explainable Artificial Intelligence(Mdpi, 2023) Akbulut, Sami; Yagin, Fatma Hilal; Cicek, Ipek Balikci; Koc, Cemalettin; Colak, Cemil; Yilmaz, SezaiBackground: The primary aim of this study was to create a machine learning (ML) model that can predict perforated and nonperforated acute appendicitis (AAp) with high accuracy and to demonstrate the clinical interpretability of the model with explainable artificial intelligence (XAI). Method: A total of 1797 patients who underwent appendectomy with a preliminary diagnosis of AAp between May 2009 and March 2022 were included in the study. Considering the histopathological examination, the patients were divided into two groups as AAp (n = 1465) and non-AAp (NA; n = 332); the non-AAp group is also referred to as negative appendectomy. Subsequently, patients confirmed to have AAp were divided into two subgroups: nonperforated (n = 1161) and perforated AAp (n = 304). The missing values in the data set were assigned using the Random Forest method. The Boruta variable selection method was used to identify the most important variables associated with AAp and perforated AAp. The class imbalance problem in the data set was resolved by the SMOTE method. The CatBoost model was used to classify AAp and non-AAp patients and perforated and nonperforated AAp patients. The performance of the model in the holdout test set was evaluated with accuracy, F1- score, sensitivity, specificity, and area under the receiver operator curve (AUC). The SHAP method, which is one of the XAI methods, was used to interpret the model results. Results: The CatBoost model could distinguish AAp patients from non-AAp individuals with an accuracy of 88.2% (85.6-90.8%), while distinguishing perforated AAp patients from nonperforated AAp individuals with an accuracy of 92% (89.6-94.5%). According to the results of the SHAP method applied to the CatBoost model, it was observed that high total bilirubin, WBC, Netrophil, WLR, NLR, CRP, and WNR values, and low PNR, PDW, and MCV values increased the prediction of AAp biochemically. On the other hand, high CRP, Age, Total Bilirubin, PLT, RDW, WBC, MCV, WLR, NLR, and Neutrophil values, and low Lymphocyte, PDW, MPV, and PNR values were observed to increase the prediction of perforated AAp. Conclusion: For the first time in the literature, a new approach combining ML and XAI methods was tried to predict AAp and perforated AAp, and both clinical conditions were predicted with high accuracy. This new approach proved successful in showing how well which demographic and biochemical parameters could explain the current clinical situation in predicting AAp and perforated AAp.Öğe Preoperative evaluation of liver volume in living donor liver transplantation(Kare Publ, 2018) Baskiran, Adil; Kahraman, Aysegul Sagir; Cicek, Ipek Balikci; Sahin, Tolga; Isik, Burak; Yilmaz, SezaiOBJECTIVE: The aim of the present study was to retrospectively evaluate the difference between the preoperative estimated volume and the actual intraoperative graft volume determined in donor right hepatectomies and to evaluate the possible effect of age, gender, and body mass index on the difference. METHODS: A total of 225 donor hepatectomies performed at the center between 2016 and 2017 were evaluated for the study. Left hepatectomies and left lateral segmentectomies were excluded from the analysis. As a result, 174 donor right hepatectomies were included in the study. Volumetric analysis was performed with dynamic hepatic computed tomography (CT), including non-contrast analysis, followed by non-ionic, contrast-enhanced arterial, portal, and hepatic-phase, thin-slice scanning. Volumetric analysis was performed based on the CT images using automatic volume calculating software. RESULTS: The mean preoperatively estimated graft volume was 800 +/- 112 g and the mean intraoperatively measured actual graft volume was 750 +/- 131 g. There was a statistically significant difference (p=0.003). Age and body mass index had a significant impact on the discrepancy between the predicted and actual graft volume, while gender did not. CONCLUSION: A thorough preoperative evaluation of the donor graft volume should be performed in order to prevent donor morbidity and mortality, as well as small-for-size and large-for-size phenomena in the implanted grafts. Physicians working in the field of transplantation should be aware of the fact that a difference of 10% between the predicted and the actual graft volume is usually encountered.Öğe Primary Negative Prognostic Factors in Pediatric and Adult Patients Undergoing Trigger Finger Surgery(Springernature, 2024) Koroglu, Muhammed; Karakaplan, Mustafa; Yildiz, Mustafa; Eren, Mehmet; Ergen, Emre; Cicek, Ipek Balikci; Aslantuerk, OkanObjectives This study aims to investigate the negative prognostic indicators of pediatric and adult trigger finger surgery patients concerning complications, recurrence, and satisfaction. Methods A retrospective study was conducted on 61 patients with a total of 91 trigger fingers, including 31 in children and 30 in adult patients, all of whom were treated using a standardized surgical technique. The study considered several demographic and clinical factors, including age, gender, dominant hand, body mass index, occupation, history of trauma, single or multiple finger involvement, staging according to Green classification, diabetes mellitus, comorbidities, recurrence, revision surgery, utilization of non -surgical treatment methods, need for rehabilitation after surgery, time to return to work, the time interval from clinic initiation to the surgery, satisfaction and the duration of the follow-up period. In addition, the quick version of the disabilities of the arm, shoulder, and hand (QDASH); and the visual analog scale (VAS) were used to assess patients' data. Results In adult patients, a statistically significant relationship was observed between the increasing grade of the Green stage and complication rate (p<0.001), recurrence (p<0.001), and lower satisfaction (p<0.001). No statistically significant relationship was identified between Green's classification and complications (p=0.129), recurrence (p=0.854), or satisfaction (p=0.143) in pediatric patients. While a statistically significant relationship existed between the time interval from clinic initiation to surgery and complications (p=0.033) in adult patients, no significant relationships were observed for recurrence or satisfaction. Conversely, there was no statistically significant relationship between the time interval from clinic initiation to surgery and complications, recurrence, or satisfaction in pediatric patients. Conclusion This study demonstrates that increasing the grade of the Green stage and duration of symptoms before surgery were the substantial factors contributing to prognosis in adult patients but not in pediatric patients. These findings can assist physicians during patients' treatment management. We suggest that physicians consider these factors for patients' satisfaction.
