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Aim: The aim of this study to determine the incidence of aberrant right subclavian artery (ARSA) and aberrant left subclavian artery (ALSA), their diameter, angle at their point of origin, distance between them. The cases included in the study were also examined for atrial septal defect (ASD), aneurysm, Kommerell's diverticulum, dysphagia, dyspnea, atherosclerotic heart disease, and hypertension. Material and Method: This study is a retrospective cross-sectional study conducted at Inonu University Faculty of Medicine Turgut Özal Medical Center. Within the scope of the study, The images of 2365 patients who applied to the Department of Radiology for contrastenhanced thoracic CT arterial phase imaging were examined. As a result of the review, 52 cases (20 men and 32 women) with ARSA and ALSA were identified and included in the study. Results: Among the examined images, ARSA was detected in 46 (1.9%) patients, while ALSA was detected in 6 (0.2%) patients. In ARSA cases, ASD and aneurysm were each found in 3 cases. Kommerell's diverticulum was not found in ARSA cases. In ALSA cases, aneurysm was found in 1 case, while Kommerell's diverticulum was found in 5 patients (83.3%). No evidence of ASD was found in ALSA cases. At the origin points, the average diameter of ARSA was 11.7 mm and ALSA was 12.55 mm, with average angles were 76.39° and 60.27°, respectively. The average distance between the right subclavian artery and the left subclavian artery in ARSA cases was 7.27 mm. In ALSA cases, the average distance between the left subclavian artery and the truncus brachiocephalicus was 10.9 mm. Conclusion: The incidence of ARSA and ALSA in the studied population was 1.9% and 0.2%, respectively. The detailed anatomical characteristics provided in this study can aid in the planning and execution of vascular surgeries involving subclavian arteries. [ABSTRACT FROM AUTHOR]

Early risk factor detection is essential for managing and treating cardiovascular disease (CVD), a global health issue. Studies have shown that waist circumference (WC) and waist hip ratio (WHR) are better at identifying CVD than BMI. The study uses Random Forest (RF) machine learning to identify characteristics that affect WHR, an indication of CVD. Isfahan Cardiovascular Research Centre in Iran provided the dataset, which includes sex, family history, diabetes, WHR, smoking, systolic blood pressure, and total cholesterol. Statistical analyses employed Yates' correction and Pearson chi-squared tests. Modeling with RF yielded accuracy, balanced accuracy, sensitivity, specificity, PPV, NPV, and F1 score from performance metrics. Finally, variable significance values determined the dependent variable's most relevant variables. WHR and other variables are statistically significantly correlated. Random Forest machine learning predicts high WHR with high accuracy, sensitivity and specificity. The most important variables of the prediction model are female sex, smoking status and blood pressure ranges. In conclusion, the global burden of CVD and the necessity of early diagnosis are underlined. The role of WHR along with BMI and waist circumference in the assessment of cardiovascular risk is emphasised. The study concludes that the machine learning model can effectively predict high WHR, aid CVD risk management and facilitate personalised treatment plans. The results contribute to a better understanding of the factors influencing high WHR and can guide healthcare professionals in the comprehensive assessment and management of cardiovascular risks. [ABSTRACT FROM AUTHOR]

Polycystic ovary syndrome (PCOS) is a complex condition characterized by high male hormone levels, irregular menstrual cycles, lack of ovulation, and sometimes small ovarian cysts. Often underdiagnosed, PCOS leads to significant health issues, making timely and efficient identification crucial. Recently, machine learning (ML) has shown promise in medical diagnoses, but the perceived "black box" nature of ML models necessitates explanations of key parameters influencing predictions. This study aims to provide global explanations using SHapley Additive exPlanations (SHAP) to ensure the efficiency, effectiveness, and reliability of the ML model. An open-access dataset with 300 PCOS patients was utilized to predict whether a patient's luteinizing hormone (LH) to follicle-stimulating hormone (FSH) ratio is up to 1 or more than 1. The study employed ML classifiers including AdaBoost, XGBoost, CatBoost, and Bagging methods, with Bagging performing the best. The modeling process used a 5-fold cross-validation approach, splitting the dataset into 80% training and 20% testing sets. The model's performance was evaluated using accuracy (ACC), balanced accuracy (b-ACC), specificity (SP), sensitivity (SE), negative predictive value (npv), positive predictive value (ppv), and F1-score. The Bagging method yielded the following performance metrics: ACC (99.0%), b-ACC (99.0%), SE (98.8%), SP (99.1%), ppv (97.7%), npv (99.5%), and F1-score (98.3%). SHAP analysis identified the top predictors for distinguishing between LH: FSH ratio categories as TTng/dL, BMI, AMH, age, family history, and menstrual cycle regulation. This study demonstrates that incorporating SHAP explanations enhances the interpretability and reliability of ML models in diagnosing PCOS. [ABSTRACT FROM AUTHOR]

Pancreatic cancer is one of the most common solid tumours and pancreatic ductal adenocarcinomas (PDACs) are the most common type of pancreatic cancer, accounting for 95% of cases. The aim of this study was not to analyze the risk factors that may be associated with PDAC, the most common type of pancreatic cancer, but to determine how these factors, taken together, affect the diagnosis of the disease. An open-access dataset containing PDAC and associated factors was used in the study. Mann Whitney U test was used in data analysis. ROC analysis was used to determine the power of the variables individually and together, in discriminating PDAC. According to the results of ROC analysis for control-PDAC and benign hepatobiliary disease-PDAC groups, the discriminatory power of the creatinine variable between the groups was found to be quite low (AUC control-PDAC=0.531, AUC benign hepatobiliary disease-PDAC=0.514, respectively). The AUC values obtained from LYVE1, REG1B, TFF1 variables for control-PDAC and benign hepatobiliary disease-PDAC comparisons are high and the discriminative power of the variables is said to be high. The results of ROC analyses performed with variables obtained from binary and triple combinations of these variables were also found to be quite high. Based on the findings presented in the article, it can be concluded that the individual components connected with the disease, when utilized in isolation for diagnostic purposes, exhibit low impact. However, when these aspects are collectively included, there is an observed enhancement in diagnostic accuracy. Hence, it is advisable to collectively evaluate the elements believed to be linked with the disease during the diagnostic process, as this approach may yield more precise outcomes. [ABSTRACT FROM AUTHOR]

Objectives: Hepatitis B virus (HBV), one of the biggest health problems of the world and our country, still constitutes the largest cause of liver failure and liver transplantation in the world. Here, we will introduce the HBV virus closely and share the health problems of HBV in the world and in our country in the literature data. Methods: İnönü University Liver Transplant Institute Patients who underwent liver transplantation due to any reason related to HBV were included in the study. Patients who underwent liver transplantation due to liver diseases caused by HBV in our institute between 2009 and 2023 were included in the study. . A total of 3679 patients underwent liver transplantation between 2002 and 2024. Of these patients, 1275 patients were operated on with the diagnosis of HBV. When 530 patients whose data were not available and 49 patients who were retransplanted were excluded from the study, a total of 695 patients were included in the study. Results: Treatment is given in combination with antiviral and HBIG. It is available in centers where powerful antivirals are used alone. Although the approaches of the centers vary, patients who have had a liver transplant due to HBV definitely need postoperative medical treatment to prevent HBV recurrence. Conclusion: Patients who have undergone liver transplantation due to HBV must have their Hbs-ag level checked when they are discharged from the hospital. Informing the patient about HBV recurrence and medical treatment provides a more meticulous medical treatment. [ABSTRACT FROM AUTHOR]

Biomedical text document classification is an essential task within Natural Language Processing (NLP), with applications ranging from sentiment analysis to authorship identification. Despite advancements in traditional machine-learning algorithms like Support Vector Machines (SVM) and Logistic Regression, challenges such as data sparsity and high dimensionality persist. Recent years have seen a surge in the use of deep learning models to mitigate these issues. This study aims to conduct a comparative analysis of various machine-learning algorithms for classifying biomedical text documents. The study employs the "Medical Text Dataset - Cancer Doc Classification" from Kaggle, comprising 7570 biomedical text documents labeled into three types of cancer (colon, lung, and thyroid). A preprocessing pipeline involving tokenization, stop-word removal, and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization is applied. Algorithms including Logistic Regression, SVM, and Multinomial Naive Bayes are evaluated through 5-fold cross-validation. Performance metrics like accuracy, precision, recall, F1 score, and area under the ROC curve (AUC ROC) are employed. Logistic Regression outperforms the other algorithms with an accuracy of 78.3% and an AUC ROC of 88.59%. SVM and Multinomial Naive Bayes follow with lower performance metrics. Hyperparameter tuning further enhances the performance of the algorithms, particularly Logistic Regression. The study makes a significant contribution to the field of biomedical text classification by systematically comparing machine-learning algorithms. Logistic Regression emerges as the most effective, emphasizing the importance of algorithm selection and hyperparameter tuning in machine learning applications within this domain. [ABSTRACT FROM AUTHOR]

Aim: As people's quality of life and habits have changed, Coronary Artery Disease (CAD) has become the leading cause of death globally. It is a complicated cardiac disease with various risk factors and a wide range of symptoms. An early and accurate diagnosis of CAD allows for the quick administration of appropriate treatment, which contributes to a decreased mortality rate. Machine learning (ML) algorithms for CAD prediction and treatment decisions are quickly being developed and implemented in clinical practice. Predictive models based on machine learning algorithms may aid health personnel in the early diagnosis of CAD, lowering mortality. Thus, this study goal is to forecast the elements that may be connected with CAD using tree-based approaches, which are one of the machine learning methods, and to discover which factor is more effective on CAD. Materials and Methods: The open-access heart disease dataset was used within the scope of the study to investigate the risk factors related with CAD. The data set used contains the values of 333 patients, as well as 20 input and 1 target variables. The 10-fold cross validation approach was employed in the modeling, and the data set was divided as 80%: 20% as training and test datasets. For model assessment, the measures of accuracy (ACC), balanced accuracy (b-ACC), sensitivity (SE), specificity (SP), positive predictive value (ppv), negative predictive value (npv), and F1-score were utilized. Results: The values of ACC, b-ACC, SE, SP, ppv, npv, and F1-score performance metrics were 9 98.5%, 98.8%, 97.7%, 100%, 100%, 95.8% and 98.8%, respectively, as a consequence of the estimate model results created with the XGBoost approach, which has the best performance among tree-based models. When the groups with or without CAD were compared, a statistically significant difference was found in terms of the age variable. There is also a significant relationship between the active, lifestyle, ihd, dm, ecgpatt, qwave variables and the presence/absence of the CAD variable. When the variable significance values obtained as a result of modeling with the highest performing XGBoost are examined, it is seen that the variables that most associated with CAD are ekgpatt: normal, ekgpatt: ST-depression, ekgpatt: T-inversion, qwave: yes, age, bpdias, height, LDL, HR, IVSD: with LVH, bpsyDM. Conclusion: According to the performance criteria of the forecasting models used, CAD gave distinctively successful results in forecasting. By identifying risk factors associated with CAD, the proposed machine learning models can provide clinicians with practical, cost-effective and beneficial assistance in making accurate predictive decisions. [ABSTRACT FROM AUTHOR]

Aim: To classify angina pectoris (AP) in women by applying the Bagged CART approach, which is one of the machine learning (ML) methods, to the open-access AP dataset. Another aim is to reveal the risk factors associated with AP in women through modeling. Materials and Methods: In the current study, modeling was done with the Bagged CART technique utilizing an open-access data set containing the factors associated with AP. Model results were assessed with accuracy (ACC), sensitivity (Sen), balanced accuracy (BACC), positive predictive value (PPV), specificity (Spe), negative predictive value (NPV), and F1-score performance criteria. In addition, a 5-fold cross-validation approach was applied in the modeling phase. Finally, variable importance was derived with modeling. Results: ACC, BACC, Sen, Spe, PPV, NPV, and F1-score from Bagged CART modeling were 98.5%, 98.5%, 99.0%, 98.0%, 98.0%, 99.0%, and 98.5%, respectively. Depending on the variable importance values calculated for the input variables investigated in the current study, age, family history of myocardial infarction: yes, the average number of cigarettes smoked per day smoking status: current, family history of angina: yes, hypertensive condition: moderate, smoking status: ex, hypertensive condition: mild, family history of stroke: yes, whether the woman has diabetes: yes were obtained as the most important variables associated with AP. Conclusion: With the ML model used, the AP dataset was classified successfully, and the associated risk factors were revealed. ML models can be used as clinical decision support systems for early diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Ovarian cancer is one of the most common types of gynecological malignancies with its high mortality rate, silent and occult tumor growth, late onset of symptoms and diagnosis in advanced stages. Therefore, the need to develop new diagnostic techniques to predict the course of the disease and the prognosis of this malignancy has increased. In this study, ovarian cancer and benign ovarian tumor samples will be classified to create an accurate diagnostic predictive model using the machine learning method XGBoost and Stochastic Gradient Boosting and disease-related risk factors will be determined. This current study considered the openaccess ovarian cancer and benign ovarian tumor samples data set. For this purpose, data from 349 patients were included. The data set was divided as 80:20 as a training and test dataset. XGBoost and Stochastic Gradient Boosting were constructed for the classification via five-fold cross-validation. Accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, and negative predictive value performance metrics were evaluated for model performance. Among the performance criteria in the test stage obtained from the XGBoost model that has the best classification result; accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were obtained as 89.5%, 88.7%, 85.7%, 91.7%, 85.7%, 91.7%, and 85.7%, respectively. According to the variable importance obtained as a result of the model, the variables most associated with the diagnosis were CA72-4, HE4, LYM%, ALB, EO%, BUN, RBC, NEU, and MCV, respectively. The applied machine learning model successfully classified ovarian cancer and created a highly accurate diagnostic prediction model. The results from the study revealed effective parameters that can diagnose ovarian cancer with high accuracy. With the parameters determined as a result of the modeling, the clinician will be able to simplify and facilitate the decision-making process for the diagnosis of ovarian cancer. [ABSTRACT FROM AUTHOR]

Aim: The goal of this study is to compare the performances of Logistic Regression (LR), Artificial Neural Networks (ANN) and Decision Tree models, which are machine learning classification methods, in the diagnosis of type 2 Diabetes Mellitus (DM) and to determine the most successful method. It is also the examination of risk factors affecting type 2 DM using these models Materials and Methods: The study's data was collected from patients who visited the Diabetes and Thyroid polyclinic at the Inonu University Faculty of Medicine Turgut Ozal Medical Center, Department of Internal Medicine. The k-Nearest Neighbor algorithm, which is one of the missing value assignment methods, was used to eliminate the problems related to missing values. Sensitivity, accuracy, precision, specificity, AUC F1-score, and classification error were used as performance evaluation criteria. Evolutionary algorithm parameter optimization method was used to optimize the parameters of the ANN model. Missing value assignment, modeling and parameter optimization were done with Rapidminer Studio Free version 8.1. Results: Among the three methods applied in the diagnosis of type 2 DM, the ANN gave the best classification performance. The accuracy, sensitivity, selectivity, precision, F1-score, AUC and classification error values obtained from this method are respectively; 98.94%, 100%, 97.73%, 98.04%, 99.01%, 0.978 and 1.06. For the ANN method, the importance values of the gender, long-term drug use, family history, concomitant disease, cortisone use, stress factor, high blood pressure, smoking, high cholesterol, heart disease, exercise status, carbohydrate use, alcohol consumption, vegetable use, meat use, age, weight, height, starting age, daily bread consumption, LDL, HDL, Total Cholesterol, Triglyceride, Fasting blood sugar the importance values of independent variables are respectively; 0.017, 0.009, 0.013, 0.017, 0.008, 0.016, 0.008, 0.006, 0.053, 0.024, 0.023, 0.040, 0.007, 0.020, 0.007, 0.046, 0.083, 0.049, 0.024, 0.066, 0.084, 0.083, 0.020, 0.031, 0.244. Conclusion: According to the performance criteria obtained from the three classification models used to predict type 2 DM; it has been found that the best classification performance belongs to the ANN model. According to the ANN method, the three most important risk factors that may cause type 2 DM were found to be fasting blood glucose, LDL, and HDL, respectively. [ABSTRACT FROM AUTHOR]

In this study, it was aimed to determine the rational drug use knowledge level of Syrians under temporary protection who applied to migrant health centers in Malatya province and related factors. A total of 983 Syrian patients under temporary protection in four Migrant Health Centers in Malatya were included in the study. Socio-demographic characteristics form and Rational Drug Use Scale were used as data collection tools. The mean duration of stay of the population included in the study in Turkey was 5.8±2 (1-12, median 6) years, and the mean duration of stay in Malatya was 5.2±2.1(1-10, median 6) years. According to the questionnaire on the Rational Drug Use Scale, the average score of the participants was approximately 6 points behind the cut-off value of 34 points. Only 23.1% of them were able to score 35 and above. While the scores of the patients did not differ according to gender, a weak correlation was found with age. In addition, it was determined that education level, year lived in Turkey, marital status and regular working life in Syria and living in a village or town were effective factors on rational drug use scores. According to the findings of this study, it was determined that the rational drug use knowledge level of the participants was low. [ABSTRACT FROM AUTHOR]

The goal of this study is to compare the performance of the deep survival model and the Cox regression model in an open-access Lung cancer dataset consisting of survivors and dead patients. In the study, it is applied to an open access dataset named "Lung Cancer Data" to compare the performances of the CPH and deepsurv models. The performance of the models is evaluated by C-index, AUC, and Brier score. The concordance index of the deep survival model is 0.64296, the Brier score was 0.128921, and the AUC was 0.6835. With the Cox regression model, the concordance index is calculated as 0.61445, brier score 0.1667, and AUC 0.5832. According to the Concordance index, brier score, and AUC criteria, the deep survival model performed better than the cox regression model. DeepSurv's forecasting, modeling, and predictive capabilities pave the path for future deep neural network and survival analysis research. DeepSurv has the potential to supplement traditional survival analysis methods and become the standard method for medical doctors to examine and offer individualized treatment alternatives with more research. [ABSTRACT FROM AUTHOR]

Aim: 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. [ABSTRACT FROM AUTHOR]

Objective: 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. [ABSTRACT FROM AUTHOR]

The 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. [ABSTRACT FROM AUTHOR]

Objective: 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., Competing Interests: Conflict of Interest: No conflict of interest was declared by the authors.