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Öğe A hybrid machine learning model combining association rule mining and classification algorithms to predict differentiated thyroid cancer recurrence(Frontiers Media Sa, 2024) Atay, Feyza Firat; Yagin, Fatma Hilal; Colak, Cemil; Elkiran, Emin Tamer; Mansuri, Nasrin; Ahmad, Fuzail; Ardigo, Luca PaoloBackground Differentiated thyroid cancer (DTC) is the most prevalent endocrine malignancy with a recurrence rate of about 20%, necessitating better predictive methods for patient management. This study aims to create a relational classification model to predict DTC recurrence by integrating clinical, pathological, and follow-up data.Methods The balanced dataset comprises 550 DTC samples collected over 15 years, featuring 13 clinicopathological variables. To address the class imbalance in recurrence status, the Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTE-NC) was utilized. A hybrid model combining classification algorithms with association rule mining was developed. Two relational classification approaches, regularized class association rules (RCAR) and classification based on association rules (CBAR), were implemented. Binomial logistic regression analyzed independent predictors of recurrence. Model performance was assessed through accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score.Results The RCAR model demonstrated superior performance over the CBAR model, achieving accuracy, sensitivity, and F1 score of 96.7%, 93.1%, and 96.7%, respectively. Association rules highlighted that papillary pathology with an incomplete response strongly predicted recurrence. The combination of incomplete response and lymphadenopathy was also a significant predictor. Conversely, the absence of adenopathy and complete response to treatment were linked to freedom from recurrence. Incomplete structural response was identified as a critical predictor of recurrence risk, even with other low-recurrence conditions.Conclusion This study introduces a robust and interpretable predictive model that enhances personalized medicine in thyroid cancer care. The model effectively identifies high-risk individuals, allowing for tailored follow-up strategies that could improve patient outcomes and optimize resource allocation in DTC management.Öğe A Nationwide Assessment of Turkish Society's Knowledge and Attitudes Toward Xenotransplantation(Wiley, 2025) Akbulut, Sami; Kucukakcali, Zeynep; Ozer, Ali; Colak, CemilBackground: This study aimed to assess public perceptions, awareness, and attitudes toward xenotransplantation (XTx) and organ donation in Turkey by examining the influence of demographic, socioeconomic, and religious factors to identify barriers and facilitators to organ donation and XTx acceptance Methods: This cross-sectional survey was conducted with 10 650 participants, selected through stratified sampling to ensure national representation. Data collection was performed via Computer-Assisted Personal Interviewing (CAPI), with structured questionnaires designed to evaluate participants' perspectives on organ donation, XTx, and religious influences, and comparisons were made based on age groups, geographical region, sectarian affiliation, education level, belief categories, and economic status. ResultsOrgan donation rates were low across all demographic groups, with notable differences by geographical region, education level, income, age, and religious beliefs. The highest organ donation rate was in Central Anatolia (0.9%), while Southeastern Anatolia had the lowest (0.0%) (p = 0.014). Higher education (p = 0.001) and income levels (p = 0.01) correlated with greater organ donation support. Younger individuals (18-24 years) were less religiously observant, while older participants (65+) displayed the highest religious adherence (p = 0.022). Acceptance of XTx from halal animals was highest in the Aegean region (43.0%) (p = 0.001) and among participants with lower religious adherence (27.4%) (p = 0.004). Approval for XTx from non-halal animals was significantly lower, particularly among highly religious individuals (23.9%). Awareness of XTx-related studies was lowest among participants aged 65+ (9.4%) (p < 0.001) and highest among Maliki participants (27.3%). Conclusion: This study highlights the influence of demographic, socioeconomic, and religious factors on public attitudes toward organ donation and XTx in Turkey. These findings offer critical insights for policymakers and healthcare professionals to design culturally adaptive strategies that improve organ donation rates and foster XTx acceptance.Öğe A novel software for method comparison: MCS (method comparison software)-assessing agreement between estimated fetal weights calculated by Hadlock I-V formulas and birth weight(Springer Heidelberg, 2024) Yasar, Seyma; Arslan, Ahmet Kadir; Polat, Busra Berfin; Melekoglu, Rauf; Colak, Cemil; Yologlu, SaimIntroductionThe evaluation of the performance of new methods, expected to provide cheaper and faster results than existing (reference) methods in the health field, is based on comparing the results obtained with this new method to those obtained with the existing method. The primary aim of this study is to examine the correlational and absolute agreement between measurement methods in clinical studies using Bland-Altman analysis and methodological (Ordinary Least Squares, Weighted Ordinary Least Squares, Deming, Weighted Deming, Passing-Bablok, Theil-Sen, and Passing-Bablok for Large Data Sets.) methods, and the secondary aim is to compare the accuracy and precision of Hadlock (I-V) formulas used for fetal weight estimation.Materials and methodsThe study was conducted on singleton pregnancies examined in the Prenatal Diagnosis and Treatment Unit of the Department of Obstetrics and Gynecology at Inonu University Faculty of Medicine and who gave birth in the Obstetrics Unit between 01.01.2020 and 01.09.2023, whose gestational ages were confirmed by first-trimester ultrasonography. Estimated fetal weights were calculated using Hadlock (I-V) formulas, and the agreement of these weights with birth weight was evaluated with Bland-Altman method.ResultsThe comparison of estimated fetal weights calculated using Hadlock formulas with birth weight was analyzed using Bland-Altman analysis, ICC, and CCC values along with regression analyses. According to the mean difference values obtained by Bland-Altman analysis, the estimated fetal birth weights obtained by the Hadlock IV formula were most consistent with the actual birth weights.ConclusionsThe estimated fetal weights obtained using the Hadlock IV formula resulted in the closest measurements to the birth weight. This study showcases the efficacy of a new web-based software, Method Comparison Software (MCS), which can be utilized for evaluating the agreement between different methods in clinical measurements.Öğe Accuracy is not enough: explainable boosting machine model and identification of candidate biomarkers for real-time sepsis risk assessment in the emergency department(Bmc, 2025) Yagin, Fatma Hilal; Aygun, Umran; Colak, Cemil; Alkhalifa, Amal K.; Alzakari, Sarah A.; Aghaei, MohammadrezaBackgroundSepsis poses a significant threat in emergency settings, necessitating tools for early and interpretable risk assessment. This study aimed to develop a robust explainable boosting machine (EBM) model, one of the explainable artificial intelligence (XAI) technologies, to construct a predictive model that balances high accuracy and clinical interpretability for use in emergency departments (EDs) and to examine candidate biomarkers.MethodsThe study identified a significant class imbalance problem in the sepsis distribution among 560 sepsis and 1012 non-sepsis patients. To address the imbalance issue, SMOTE-NC was applied in the training data. The data was divided into two parts, 80% training and 20% testing. To ensure the reliability of the models and to report unbiased results, this process was repeated 100 times and the average performance was reported. To determine the best model for sepsis prediction, five different models (AdaBoost, Gradient Boosting, CatBoost, LightGBM, and EBM) were trained, and their performances were evaluated. In the last stage, we presented local and global explanations of EBM.ResultsThe EBM model achieved the highest success by reaching 79.1% F1-score, 80.9% sensitivity, and 84.8% AUC after resampling. In the global explanations, the variables with the highest weights in the model's decision process were identified as positive blood culture, oxygen saturation, and procalcitonin, respectively.ConclusionThe EBM model accurately predicts sepsis risk based on clinically relevant biomarkers. The model's high performance and inherent transparency can foster trust among clinicians and facilitate its integration into emergency department workflows for real-time decision support.Öğe Acute and Subacute Effects of Low Versus High Doses of Standardized Panax ginseng Extract on the Heart: An Experimental Study(Humana Press Inc, 2019) Parlakpinar, Hakan; Ozhan, Onural; Ermis, Necip; Vardi, Nigar; Cigremis, Yilmaz; Tanriverdi, Lokman H.; Colak, CemilPanax ginseng is commonly used in Chinese medicine and Western herbal preparations. However, it has also been recently noted to be associated with some cardiac pathologies-including cardiogenic shock due to acute anterior myocardial infarction, trans-ischemic attack, and stent thrombosis. This study was aimed to elucidate acute and subacute effects of the low and high doses of standardized Panax ginseng extract (sPGe) on cardiac functions. Rats were randomly assigned to control group, acute low-dose group (ALD), subacute low-dose group (SALD), acute high-dose group (AHD), and subacute high-dose group (SAHD). The cardiac effects of sPGe were evaluated using hemodynamic, biochemical, echocardiographic, genetic, and immunohistopathologic parameters. Mean blood pressures were significantly lower in all sPGe-treated groups compared with the control group. Troponin I and myoglobin levels were increased in the SALD, AHD, and SAHD groups. Mitral E-wave velocity was reduced after sPGe administration in all the groups. Acidophilic cytoplasm and pyknotic nucleus in myocardial fibers were observed in AHD and SAHD groups. Cu/Zn-SOD1 gene expressions were significantly higher in the sPGe-treated groups whereas caveolin 1 and VEGF-A gene expressions were not changed. According to our results, sPGe may have a potential effect to cause cardiac damage including diastolic dysfunction, heart failure with preserved ejection fraction, and reduction of blood pressure depending on the dose and duration of usage. Healthcare professionals must be aware of adverse reactions stemming from the supplementation use, particularly with cardiac symptoms.Öğe Adenosine deaminase level in the serum of the patients Toxoplasma gondii seropositive and Giardia intestinalis(Academic Journals, 2009) Karaman, Uelkue; Beytur, Leyla; Kiran, Tugba Raika; Colak, CemilAdenosine deaminase (ADA) is an aminohydrolase making adenosine, deoksiadenozini inozin, and deocsiniozine deaminise irreversibly and plays role in the catabolism of purine nucleotids. Toxoplasma gondii is a zoonoses intracellular parasite that causes infection in animals and humans. This parasite encompasses enzymes that produce free radicals such as superoxide and hydrogen peroxide. In addition, Giardia intestinalis is another parasite that causes irritations in mucosa, over mucus discharge, aggravating former inflammations, and various absorption defects. In the present study, it has been aimed to compare ADA levels between T. gondii seropositive (IgG seropositive but symptomless patients), G. intestinalis positive patients, and control group. Thus, ADA levels between 32 patients being T. gondii seropositive and 29 controls and between 50 patients' G. intestinalis positive and 40 controls have been evaluated. The results were analyzed using independent samples t-test at the level of p < 0.05. According to this, in the statistical comparison between the parameters of patient and control groups, a meaningful decrease could be determined in ADA levels. This situation can be commented in the way that toxoplasmosis infection being inactive does not necessarily cause an increase in T lymphocytes. In addition, this decrease can be due to increasing oxidative stress in parasitic infections.Öğe Alpha lipoic acid decreases neuronal damage on brain tissue of STZ-induced diabetic rats(Pergamon-Elsevier Science Ltd, 2022) Tanbek, Kevser; Ozerol, Elif; Yilmaz, Umit; Yilmaz, Nesibe; Gul, Mehmet; Colak, CemilNeuropathy that develops due to diabetic complications causes cognitive impairment due to functional and structural damage. The aim of this study was to evaluate the biochemical, histological and physiological effects of Alpha Lipoic Acid (ALA) against brain tissue damage caused by diabetes. Fourty male Wistar albino rats were separated into four groups as control, diabetes mellitus (DM), ALA and DM+ALA. Single dose of 50 mg/kg intraperitonal streptozotocin (STZ) was used to induce DM. For six weeks, ALA (100 mg/kg/day) was administered to the ALA and DM+ALA groups. At the end of the six week rats were sacrificed by collecting blood samples and collected brain tissues (hippocampus, cortex, hippotalamus and striatum) were histologically evaluated in addition to the oxidant-antioxidant parameters. ALA administration showed significant improvement in cognitive functions evaluated by MWM in rats with diabetes mellitus (p < 0.05). SOD, CAT, GSH-Px activities, which were decreased in the DM group compared to the control group, increased statistically significantly in rats in DM+ALA group (p < 0.05). While MDA and PC levels increased in the DM group, they decreased statistically significantly in the DM+ALA group (p < 0.05). According to the histological examinations made by light and electron microscopies, it was determined that the ultrastructural damage and degeneration findings observed in the sections of the DM group were significantly ameliorated in the sections of rats in the DM+ALA group. ALA may be effective in restoring cell damage and cognitive functions in brain tissue with its antioxidant and neuroprotective effects without showing antidiabetic effects.Öğe The alterations in serum ghrelin and leptin levels after intracerebroventricularly irisin infusion in rats(Wiley-Blackwell, 2015) Tekin, Suat; Erden, Yavuz; Sandal, Suleyman; Ozyalin, Fatma; Colak, Cemil[Abstract Not Available]Öğe Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study(Frontiers Media Sa, 2024) Yilmaz, Rustem; Yagin, Fatma Hilal; Colak, Cemil; Toprak, Kenan; Samee, Nagwan Abdel; Mahmoud, Noha F.; Alshahrani, Amnah AliIntroduction Acute heart failure (AHF) is a serious medical problem that necessitates hospitalization and often results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Unfortunately, there is not yet a fast and accurate laboratory test for identifying AHF. The purpose of this research is to apply the principles of explainable artificial intelligence (XAI) to the analysis of hematological indicators for the diagnosis of AHF. Methods In this retrospective analysis, 425 patients with AHF and 430 healthy individuals served as assessments. Patients' demographic and hematological information was analyzed to diagnose AHF. Important risk variables for AHF diagnosis were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) feature selection. To test the efficacy of the suggested prediction model, Extreme Gradient Boosting (XGBoost), a 10-fold cross-validation procedure was implemented. The area under the receiver operating characteristic curve (AUC), F1 score, Brier score, Positive Predictive Value (PPV), and Negative Predictive Value (NPV) were all computed to evaluate the model's efficacy. Permutation-based analysis and SHAP were used to assess the importance and influence of the model's incorporated risk factors. Results White blood cell (WBC), monocytes, neutrophils, neutrophil-lymphocyte ratio (NLR), red cell distribution width-standard deviation (RDW-SD), RDW-coefficient of variation (RDW-CV), and platelet distribution width (PDW) values were significantly higher than the healthy group (p < 0.05). On the other hand, erythrocyte, hemoglobin, basophil, lymphocyte, mean platelet volume (MPV), platelet, hematocrit, mean erythrocyte hemoglobin (MCH), and procalcitonin (PCT) values were found to be significantly lower in AHF patients compared to healthy controls (p < 0.05). When XGBoost was used in conjunction with LASSO to diagnose AHF, the resulting model had an AUC of 87.9%, an F1 score of 87.4%, a Brier score of 0.036, and an F1 score of 87.4%. PDW, age, RDW-SD, and PLT were identified as the most crucial risk factors in differentiating AHF. Conclusion The results of this study showed that XAI combined with ML could successfully diagnose AHF. SHAP descriptions show that advanced age, low platelet count, high RDW-SD, and PDW are the primary hematological parameters for the diagnosis of AHF.Öğe Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study (vol 11, 1285067, 2024)(Frontiers Media Sa, 2024) Yilmaz, Rustem; Yagin, Fatma Hilal; Colak, Cemil; Toprak, Kenan; Samee, Nagwan Abdel; Mahmoud, Noha F.; Alshahrani, Amnah Ali[Abstract Not Available]Öğe Angiotensin II type 2 receptor agonist treatment of doxorubicin induced heart failure(Taylor & Francis Ltd, 2023) Ermis, Necip; Ulutas, Zeynep; Ozhan, Onural; Yildiz, Azibe; Vardi, Nigar; Colak, Cemil; Parlakpinar, HakanDoxorubicin (DOX) is an anthracycline derivative used for treatment of malignancies; however, its clinical use is limited by its cardiotoxicity. We investigated the effects of angiotensin II type 2 receptor agonist compound 21 (C21) on DOX induced heart failure in rat heart. We compared C21 with losartan (LOS), an AT 1 receptor antagonist used for treating heart failure. We allocated 40 rats into five groups of eight: saline treated control group, DOX group administered a single 20 mg/kg dose of DOX, DOX + C21 group administered 0.3 mg/kg C21 for 21 days following the 20 mg/kg dose of DOX, DOX + losartan (LOS) group administered a 21 day regimen of 20 mg/kg LOS following the single dose of DOX, and a DOX + LOS + C21 group administered 0.3 mg/kg C21 and 20 mg/kg LOS for 21 days following the single dose of DOX. We assessed histopathology and conducted echocardiograpic and hemodynamic measurements. Left ventricular ejection fraction (EF) was reduced only in the DOX treated group. C21, LOS and C21 + LOS therapy prevented decreased EF due to DOX. Less histopathology was observed in the DOX + LOS + C21 group than for the other treatment groups. Application of C21 decreased DOX induced cardiac injury similar to LOS. Combined use of C21 and LOS was most beneficial for DOX induced heart failure.Öğe Application of knowledge discovery process on the prediction of stroke(Elsevier Ireland Ltd, 2015) Colak, Cemil; Karaman, Esra; Turtay, M. GokhanObjective: Stroke is a prominent life-threatening disease in the world. The current study was performed to predict the outcome of stroke using knowledge discovery process (KDP) methods, artificial neural networks (ANN) and support vector machine (SVM) models. Materials and methods: The records of 297 (130 sick and 167 healthy) individuals were acquired from the databases of the department of emergency medicine. Nine predictors (coronary artery disease, diabetes mellitus, hypertension, history of cerebrovascular disease, atrial fibrillation, smoking, the findings of carotid Doppler ultrasonography [normal, plaque, plaque + stenosis >= 50%], the levels of cholesterol and C-reactive protein) were used for predicting the stroke. Feature selection based on the Cramer's V test was carried out for reducing the predictors. Multilayer perceptron (MLP) ANN and SVM with radial basis function (RBF) kernel were used for the prediction based on the selected predictors. Results: The accuracy values were 81.82% for ANN and 80.38% for SVM in the training dataset (n = 209), and 85.9% for ANN and 84.62% for SVM in the testing dataset (n = 78), respectively. ANN and SVM models yielded area under curve (AUC) values of 0.905 and 0.899 in the training dataset, and 0.928 and 0.91 in the testing dataset, consecutively. Conclusion: The findings of the current study pointed out that ANN had more predictive performance when compared with SVM in predicting stroke. The proposed ANN model would be useful when making clinical decisions regarding stroke. (C) 2015 Elsevier Ireland Ltd. All rights reserved.Öğe Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications(Baishideng Publishing Group Inc, 2025) Akbulut, Sami; Kucukakcali, Zeynep; Colak, CemilAcute appendicitis (AAp) remains one of the most common abdominal emergencies, requiring rapid and accurate diagnosis to prevent complications and unnecessary surgeries. Conventional diagnostic methods, including medical history, clinical assessment, biochemical markers, and imaging techniques, often present limitations in sensitivity and specificity, especially in atypical cases. In recent years, artificial intelligence (AI) has demonstrated remarkable potential in enhancing diagnostic accuracy through machine learning (ML) and deep learning (DL) models. This review evaluates the current applications of AI in both adult and pediatric AAp, focusing on clinical data-based models, radiological imaging analysis, and AI-assisted clinical decision support systems. ML models such as random forest, support vector machines, logistic regression, and extreme gradient boosting have exhibited superior diagnostic performance compared to traditional scoring systems, achieving sensitivity and specificity rates exceeding 90% in multiple studies. Additionally, DL techniques, particularly convolutional neural networks, have been shown to outperform radiologists in interpreting ultrasound and computed tomography images, enhancing diagnostic confidence. This review synthesized findings from 65 studies, demonstrating that AI models integrating multimodal data including clinical, laboratory, and imaging parameters further improved diagnostic precision. Moreover, explainable AI approaches, such as SHapley Additive exPlanations and local interpretable model-agnostic explanations, have facilitated model transparency, fostering clinician trust in AI-driven decision-making. This review highlights the advancements in AI for AAp diagnosis, emphasizing that AI is used not only to establish the diagnosis of AAp but also to differentiate complicated from uncomplicated cases. While preliminary results are promising, further prospective, multicenter studies are required for large-scale clinical implementation, given that a great proportion of current evidence derives from retrospective designs, and existing prospective cohorts exhibit limited sample sizes or protocol variability. Future research should also focus on integrating AI-driven decision support tools into routine emergency care workflows.Öğe ARTIFICIAL INTELLIGENCE-ASSISTED PREDICTION OF COVID-19 STATUS BASED ON THORAX CT SCANS USING A PROPOSED META-LEARNING STRATEGY(Carbone Editore, 2022) Guldogan, Emek; Yilderim, Ismail Okan; Sevgi, Serkan; Colak, CemilBackground: Radiological techniques integrated with artificial intelligence (AI) are a promising diagnostic tool for the rapidly increasing number of COVID-19 cases today. In this study, we intended to construct an artificial intelligence-assisted prediction of COVID-19 status based on thorax computed tomography (CT) scans using a proposed meta-learning strategy. Methods: A public dataset including 1252 positive and 1230 negative thorax CT scans of SARS-CoV-2 was used in the current study. The CT images for COVID-19 status were analyzed by 26 transfer learning (TL) models. The stacking ensemble learning was used to obtain more consistent and high-performance prediction results by combining the prediction results of 26 TL models with an Results: Mobile had the best prediction with an accuracy of 0.946 (95% CI: 0.93-0.962) among the TL models. The Meta-learning model yielded the best classification accuracy of 0.993 (0.98-1), which outperformed MobileNet, the most successful architecture Conclusions: The proposed meta-model that can distinguish CT images between COVID-19 positive and abnormal/normal conditions due to other etiology of COVID-19 negative may be beneficial in such pandemics. The AI application in this study can be used in mobile, desktop, and web-based platforms to have facilitating and complementary effects on classical reporting and the current workload in radiology departments.Öğe Artificial Intelligence-Based Prediction of Covid-19 Severity on the Results of Protein Profiling(Elsevier Ireland Ltd, 2021) Yasar, Seyma; Colak, Cemil; Yologlu, SaimBackground: COVID-19 progresses slowly and negatively affects many people. However, mild to moderate symptoms develop in most infected people, who recover without hospitalization. Therefore, the development of early diagnosis and treatment strategies is essential. One of these methods is proteomic technology based on the blood protein profiling technique. This study aims to classify three COVID-19 positive patient groups (mild, severe, and critical) and a control group based on the blood protein profiling using deep learning (DL), random forest (RF), and gradient boosted trees (GBTs). Methods: The dataset consists of 93 samples (60 COVID-19 patients, 33 control), and 370 variables obtained from an open-source website. The current dataset contains age, gender, and 368 protein, used to predict the relationship between disease severity and proteins using DL and machine learning approaches (RF, GBTs). An evolutionary algorithm tunes hyperparameters of the models and the predictions are assessed through accuracy, sensitivity, specificity, precision, F1 score, classification error, and kappa performance metrics. Results: The accuracy of RF (96.21%) was higher as compared to DL (94.73%). However, the ensemble classifier GBTs produced the highest accuracy (96.98%). TGB1BP2 in the cardiovascular II panel and MILR1 in the inflammation panel were the two most important proteins associated with disease severity. Conclusions: The proposed model (GBTs) achieved the best prediction of disease severity based on the proteins compared to the other algorithms. The results point out that changes in blood proteins associated with the severity of COVID-19 may be used in monitoring and early diagnosis/treatment of the disease. Background: COVID-19 progresses slowly and negatively affects many people. However, mild to moderate symptoms develop in most infected people, who recover without hospitalization. Therefore, the development of early diagnosis and treatment strategies is essential. One of these methods is proteomic technology based on the blood protein profiling technique. This study aims to classify three COVID-19 positive patient groups (mild, severe, and critical) and a control group based on the blood protein profiling using deep learning (DL), random forest (RF), and gradient boosted trees (GBTs). Methods: The dataset consists of 93 samples (60 COVID-19 patients, 33 control), and 370 variables obtained from an open-source website. The current dataset contains age, gender, and 368 protein, used to predict the relationship between disease severity and proteins using DL and machine learning approaches (RF, GBTs). An evolutionary algorithm tunes hyperparameters of the models and the predictions are assessed through accuracy, sensitivity, specificity, precision, F1 score, classification error, and kappa performance metrics. Results: The accuracy of RF (96.21%) was higher as compared to DL (94.73%). However, the ensemble classifier GBTs produced the highest accuracy (96.98%). TGB1BP2 in the cardiovascular II panel and MILR1 in the inflammation panel were the two most important proteins associated with disease severity. Conclusions: The proposed model (GBTs) achieved the best prediction of disease severity based on the proteins compared to the other algorithms. The results point out that changes in blood proteins associated with the severity of COVID-19 may be used in monitoring and early diagnosis/treatment of the disease. ? 2021 Elsevier B.V. All rights reserved.Öğe Artificial intelligence-based prediction of molecular and genetic markers for hepatitis C-related hepatocellular carcinoma(Lippincott Williams & Wilkins, 2023) Colak, Cemil; Kucukakcali, Zeynep; Akbulut, SamiBackground:Hepatocellular carcinoma (HCC) is the main cause of mortality from cancer globally. This paper intends to classify public gene expression data of patients with Hepatitis C virus-related HCC (HCV+HCC) and chronic HCV without HCC (HCV alone) through the XGboost approach and to identify key genes that may be responsible for HCC.Methods:The current research is a retrospective case-control study. Public data from 17 patients with HCV+HCC and 35 patients with HCV-alone samples were used in this study. An XGboost model was established for the classification by 10-fold cross-validation. Accuracy (AC), balanced accuracy (BAC), sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were utilized for performance assessment.Results:AC, BAC, sensitivity, specificity, positive predictive value, negative predictive value, and F1 scores from the XGboost model were 98.1, 97.1, 100, 94.1, 97.2, 100, and 98.6%, respectively. According to the variable importance values from the XGboost, the HAO2, TOMM20, GPC3, and PSMB4 genes can be considered potential biomarkers for HCV-related HCC.Conclusion:A machine learning-based prediction method discovered genes that potentially serve as biomarkers for HCV-related HCC. After clinical confirmation of the acquired genes in the following medical study, their therapeutic use can be established. Additionally, more detailed clinical works are needed to substantiate the significant conclusions in the current study.Öğe Assessment of Association Rules based on Certainty Factor: an Application on Heart Data Set(Ieee, 2019) Akbas, Kubra Elif; Kivrak, Mehmet; Arslan, A. Kadir; Colak, CemilAssociation rules mining is one of the uttermost applied techniques in data mining and artificial intelligence. Support and confidence are two basic measures employed in the evaluation of association rules. The rules obtained with these two values are often correct; however, they are not strong rules. Most of the rules, especially with a high support value, are misleading. For this reason, there are many interestingness measures proposed to achieve stronger rules. In this study it is aimed to establish strong association rules with variables in open sourced heart data set. In the current study, Apriori algorithm was used to obtain the rules. As a result of the analysis, only 55 confidence and support criteria were taken into consideration. For more powerful rules, certainty factor was used as one of the interestingness measure proposed in the literature, and it was concluded that only 26 of these rules were strong. As a result of the analysis of the findings obtained in the context of the research, it can be inferred that stronger rules can be obtained by using the certainty factor in association rules mining.Öğe Assessment of clinical and pathological features of patients who underwent thyroid surgery: A retrospective clinical study(Baishideng Publishing Group Inc, 2018) Emre, Arif; Akbulut, Sami; Sertkaya, Mehmet; Bitiren, Muharrem; Kale, Ilhami Taner; Bulbuloglu, Ertan; Colak, CemilAIM To evaluate whether there was any correlation between the clinical parameters and final pathological results among patients who underwent thyroid surgery. METHODS We retrospectively analyzed parameters, including age, sex, complete blood cell count parameters, nodule diameter, nodule localization, thyroid function testing, and pathology reports, in patients who underwent thyroid surgery. The patients were divided into malignant (n = 92) and benign (n = 413) groups depending on the final pathological results. Both groups were compared for demographic and clinical parameters. The Kolmogorov-Smirnov normality test was used to determine if the quantitative variables had a normal distribution. The nonparametric Mann-Whitney U test was used to compare quantitative data that were not normally distributed, and Pearson's chi-squared test was used to compare the qualitative data. The correlation between the final pathological results and fine-needle aspiration biopsy findings was calculated using the cross-tabulation method. RESULTS This study included 406 women and 99 men aged between 15 and 85 years. No significant differences were found between the groups with respect to age, sex, white blood cell count, neutrophil count, lymphocyte count, thrombocyte count, red cell distribution width, platelet distribution width, mean platelet volume, platecrit, nodule localization, and thyroid function testing. On the other hand, there were significant differences between the groups with respect to nodule size (P = 0.001), cervical lymphadenopathy (P = 0.0001) and nodular calcification (P = 0.0001). Compared with the malignant group, the benign group had a significantly greater nodule size (35.4 mm vs 27.6 mm). The best cut-off point (<= 28 mm) for nodule size, as determined by the receiver operating characteristic curve, had a sensitivity and specificity of 67.7% and 64.4%, respectively. The correlation between fine-needle aspiration biopsy and the final pathological results was assessed using the cross-table method. The sensitivity and specificity of fine-needle aspiration biopsy were 60% and 98%, respectively. CONCLUSION This study showed that significant differences existed between the malignant and benign groups with regard to nodule size, cervical lymphadenopathy, and nodular calcification.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 Hematological Predictors via Explainable Artificial Intelligence in the Prediction of Acute Myocardial Infarction(Ieee-Inst Electrical Electronics Engineers Inc, 2023) Yilmaz, Rustem; Yagin, Fatma Hilal; Raza, Ali; Colak, Cemil; Akinci, Tahir CetinAcute myocardial infarction (AMI) is the main cause of death in developed and developing countries. AMI is a serious medical problem that necessitates hospitalization and sometimes results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Many studies have been conducted on the prognosis of AMI with hemogram parameters. However, no study has investigated potential hemogram parameters for the diagnosis of AMI using an interpretable artificial intelligence-based clinical approach. The purpose of this research is to implement the principles of explainable artificial intelligence (XAI) in the analysis of hematological predictors for AMI. In this retrospective analysis, 477 (48.6%) patients with AMI and 504 (51.4%) healthy individuals were enrolled and assessed in predicting AMI. Of the patients with AMI, 182 (38%) had an ST-segment elevation MI (STEMI), and 295 (62%) had a non-ST-segment elevation MI (NSTEMI). Demographic and hematological information of the patients was analyzed to determine AMI. The XAI approach combined with machine learning approaches (Extreme Gradient Boosting, XGB; Adaptive Boosting, AB; Light Gradient Boosting Machine, LGBM) was applied for the estimation of AMI and distinguishing subgroups of AMI (STEMI and NSTEMI). The SHAP approach was used to explain the predictions intuitively. After selecting the 10 most important hematological parameters for AMI, the LGBM model achieved 83% and 74% accuracy for prediction of AMI, and distinguishing subgroups of AMI (STEMI and NSTEMI), respectively. SHAP results showed that neutrophil (NEU), white blood cell (WBC), platelet width of distribution (PDW), and basophil (BA) were the most important for AMI prediction. Mean corpuscular volume (MCV), BA, monocytes (MO), and lymphocytes (LY) were the most important hematological parameters that distinguish STEMI from NSTEMI. The proposed model serves as a valuable tool for physicians, facilitating the diagnosis, treatment, and follow-up of patients with AMI and distinguishing subgroups of AMI (STEMI and NSTEMI). Analyzing readily accessible hemogram parameters empowers medical professionals to make informed decisions and provide enhanced care to a wide range of individuals.
