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    A deep learning analysis for the effect of individual player performances on match results
    (Springer London Ltd, 2022) Yucebas, Sait Can
    Player performance is the most important factor that affects match scores. Factors affecting player performance are not the same for all players, and vary according to pitch positions. Analyzing these performance factors in relation to pitch positions can help understand which characteristics of players need to be developed in order to win. Player training can be arranged accordingly, and team tactics can be changed or improved. Although the importance of analyzing the individual performances of players according to pitch positions has been emphasized in various studies, a large amount of data available has made this analysis difficult. Machine learning can be used to overcome this difficulty. However, machine learning studies in sports mostly focus on score prediction. There is a lack of traditional and machine learning studies that examine the effect of individual player performances on game results. In this context, the datasets of the 2010 and 2014 FIFA World Cups were analyzed through multi-layer artificial neural networks. A specific model was established for each dataset by organizing relevant datasets according to year, player positions, and match levels (group-final). Rectifier Linear Unit was selected as the activation function for each model. Architecture and hyper-parameters for each model were determined through grid optimization. The factors affecting player performances were ranked by the Gedeon's relative importance calculation. The average performance indicators for the group matches are 81.34% precision, 87% recall, and 0.84 F1 score. The area under curve for the final series is 0.798.
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    A Deep Learning Model with Attention Mechanism for Dental Image Segmentation
    (Institute of Electrical and Electronics Engineers Inc., 2022) Karacan, Merter Hami; Yucebas, Sait Can
    Radiological imaging is a frequently used procedure in dental treatments. It provides information to the physician about areas of the tooth that cannot be seen from the outside. Digital radiological images can be processed with advanced computer vision techniques. In recent years, deep learning models with attention mechanisms which are mainly developed for natural language processing, have been applied to computer vision studies. In this study, three deep learning models, Vision Transformer (ViT), Segmenter and ConvNeXt were used on the segmentation of teeth and maxillomandibular region. The performance results were better than the U-Net and other benchmark models that are widely used in medical image segmentation. The IoU performance of the models, ConvNeXt, Segmenter and ViT, for the teeth segmentation was 90.77, 91.86, 92.63 respectively. In the maxillomandibular region segmentation IoU results of the models were 92.0, 95.56, 77.51. © 2022 IEEE.
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    A model for acute kidney injury in severe burn patients
    (Elsevier Sci Ltd, 2022) Karakaya, Emre; Akdur, Aydincan; Aydogan, Cem; Turk, Emin; Sayin, Cihat Burak; Soy, Ebru Ayvazoglu; Yucebas, Sait Can
    Introduction: In patients with severe burns, morbidity and mortality are high. One factor related to poor prognosis is acute kidney injury. According to the AKIN criteria, acute kidney injury has 3 stages based on urine output, serum creatinine level, and renal replacement therapy. In this study, we aimed to create a decision tree for estimating risk of acute kidney injury in patients with severe burn injuries. Methods: We retrospectively evaluated 437 adult patients with >20% total burn surface area injury who were treated at the Baskent University Ankara and Konya Burn Centers from January 2000 to March 2020. Patients who had high-voltage burn and previous history of kidney disease were excluded. Patient demographics, medical history, mechanism of injury, presence of inhalation injury, depth of burn, laboratory values, presence of oliguria, need for renal replacement therapy, central venous pressure, and prognosis were evaluated. These data were used in a decision tree method to create the Baskent University model to estimate risk of acute kidney injury in severe burn patients. Results: Our model provided an accuracy of 71.09% for risk estimation. Of 172 patients, 78 (45%) had different degrees of acute kidney injury, with 26 of these (15.1%) receiving renal replacement therapy. Our model showed that total burn surface area was the most important factor for estimation of acute kidney injury occurrence. Other important factors included serum creatinine value, burn injury severity score, hemoglobin value, neutrophil-tolymphocyte ratio, and platelet count. Conclusion: The Baskent University model for acute kidney injury may be helpful to determine risk of acute kidney injury in burn patients. This determination would allow appropriate treatment to be given to high-risk patients in the early period, reducing the incidence of acute kidney injury. (c) 2021 Published by Elsevier Ltd.
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    A new approach to the management of acute appendicitis: Decision tree method
    (W B Saunders Co-Elsevier Inc, 2022) Erkent, Murathan; Karakaya, Emre; Yucebas, Sait Can
    ABSTR A C T Background: It is important to distinguish between complicated acute appendicitis (CAA) and noncomplicated acute appendicitis (NCAA) because the treatment methods are different. We aimed to create an algorithm that determines the severity of acute appendicitis (AA) without the need for imaging methods, using the decision tree method. Methods: The patients were analyzed retrospectively and divided into two groups as CAA and NCAA. Age, gender, Alvarado scores, white blood cell values (WBC), neutrophil/lymphocyte ratios (NLR), C-reactive protein value (CRP), albumin value and CRP/Albumin ratios of the patients were recorded. Results: In the algorithm we created, the most important parameter in the distinction between CAA and NCAA is CRP. NLR is predictive in patients with a CRP value of <= 107.565 mg/L, and the critical value is NLR 2.165. In pa-tients with a CRP value of >107.565 mg/L, albumin is the determinant and the critical value is 2.85 g/dL. Age, gen -der, alvarado score and CRP/albumin ratio have no significance in distinguishing between CAA and NCAA. In the statistical analysis, there were significant differences between NCAA and CAA groups in terms of age (39.56 years vs 13,675 years), gender (48.1% male vs 71.4% male), WBC (13,891.10/mL vs 11,614.76/mL), CRP (27 mg/L vs 127 mg/L), albumin (3 g/dL vs 3 g/dL) and CRP/albumin (9.50 vs. 41). Conclusion: Thanks to the algorithm we created, CAA and NCAA distinction can be made quickly. In addition, by avoiding unnecessary surgical procedures in NCAA cases, patients' quality of life can be increased and morbidity rates can be minimized.(c) 2022 Elsevier Inc. All rights reserved.
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    A novel SVM-ID3 Hybrid Feature Selection Method to Build a Disease Model for Melanoma using Integrated Genotyping and Phenotype Data from dbGaP
    (Ios Press, 2014) Son, Yesim Aydin; Yucebas, Sait Can
    The relations between Single Nucleotide Polymorphism (SNP) and complex diseases are likely to be non-linear and require analysis of the high dimensional data. Previous studies in the field mostly focus on genotyping and effects of various phenotypes are not considered. To fill this gap a hybrid feature selection model of support vector machine and decision tree has been designed. The designed method is tested on melanoma. We were able to select phenotypic features such as moles and dysplastic nevi, and SNPs those maps to specific genes such as CAMK1D. The performance results of the proposed hybrid model, on melanoma dataset are 79.07% of sensitivity and 0.81 of area under ROC curve.
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    Analyzing Agricultural Land Price Prediction Using Linear Regression and XGBoost Machine Learning Algorithms: A Case Study of Çanakkale
    (2025) Doğan, Simge; Genç, Levent Genc; Yucebas, Sait Can; Uşaklı, Metin
    Agricultural lands are known not only as agricultural production areas but also as areas with high income expectations as an investment tool. In Turkey, recent fluctuations in economic indicators such as the euro, dollar, and gold, along with increasing investment demand, have caused agricultural land prices to rise uncontrollably. Controlling land price increases is important for preventing the misuse of agricultural lands. The sustainable management of agricultural lands and price stability are closely related to the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 and 15, “Sustainable Cities and Communities” and “Life on Land.” In this context, accurately predicting prices is important for minimizing price fluctuations in agricultural lands for investors and landowners and supporting sustainable development. In general, the Multiple Linear Regression (MLR) model is considered one of the effective traditional methods for predicting real estate prices. However, depending on the data, more reliable results can be obtained than with powerful deep learning models such as the Extreme Gradient Boosting (XGBoost) algorithm, which exhibits superior prediction performance. This study aims to compare the MLR and XGBoost algorithms to predict agricultural land prices in villages located in the central district of Çanakkale and to examine daily fluctuations in economic indicators such as the dollar, gold, and euro. The results showed that XGBoost (R2 = 0.66) has an advantage in terms of coefficient of determination values compared to MLR (R2 = 0.01). Accurate price prediction for agricultural lands will help control fluctuations in land prices. Additionally, it will support farmers and investors in making informed decisions for a sustainable agricultural economy.
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    Cost Reduction in Thyroid Diagnosis: A Hybrid Model with SOM and C4.5 Decision Trees
    (Springer International Publishing Ag, 2015) Kinaci, Ahmet Cumhur; Yucebas, Sait Can
    The main objective of this paper is to introduce a hybrid model of Self Organizing Maps (SOM) and C4.5, to reduce the costs while maintaining an acceptable diagnostic performance. In this hybrid model, SOM is used first to form clusters and then C4.5 trees specific to each cluster is constructed. The proposed hybrid model is tested on multiclass Thyroid Data and compared to standalone C4.5 tree. Costs were reduced by 22%-27% and performance results vary between 88% and 97% in terms of accuracy and 90%-97% in terms of sensitivity. Cost and performance differences between the hybrid model and standalone C4.5 found to be statistically significant according to Wilcoxon signed-rank test.
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    Discovery of Marker Genes in Adult T Cell Leukemia (ATL) Pathogenesis with Machine Learning Models and Performance Comparison
    (2025) Kılıçarslan, Sabire; Yucebas, Sait Can
    Hematologic cancers are often diagnosed after symptoms become apparent, which can make it difficult to control the disease and implement effective treatment strategies. Studying gene expression profiles is vital for early diagnosis and the development of treatment strategies for hematologic cancers such as T-cell leukemia. The motivation of this study is to reveal the molecular mechanisms in the pathogenesis of this disease by comparing the whole gene expression profile in Adult T-cell Leukemia (ATL) cells and CD4+T cells of healthy individuals. For this aim, several machine learning algorithms, Naive Bayes, K-Nearest Neighbor, Support Vector Machine, Random Forest, C4.5, Logistic Regression, Linear Discriminant Analysis and Artificial Neural Network algorithms were used. Their performance was compared on the GSE33615 dataset by using 5-fold cross validation with stratified sampling. Among these, Artificial Neural Network stood out with an AUC of 0.98 and an F1 score of 0.93. It was followed by SVM with an AUC of 0.97 and 0.957 F1 score. In addition to performance comparison, information gain ratio, SHAPLEY metric and correlation values were calculated for the detection of genes causing ATL. Among the models, the three with the highest performance (ANN, SVM, RF) were selected, and the top ten most significant genes were identified for each. Considering the intersection of these gene sets, ZSCAN18, PLK3, and NELL2 were found to be associated with the related disease. These genes may contribute to Adult T-cell Leukemia pathogenesis through their roles in cell cycle regulation, transcriptional control, and oncogenic signaling. Further investigation is needed to clarify their precise molecular mechanisms in the related disease.
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    Evaluation with Decision Trees of Efficacy and Safety of Semirigid Ureteroscopy in the Treatment of Proximal Ureteral Calculi
    (Karger, 2017) Sancak, Eyup Burak; Kilinc, Muhammet Fatih; Yucebas, Sait Can
    Purpose: The decision on the choice of proximal ureteral stone therapy depends on many factors, and sometimes urologists have difficulty in choosing the treatment option. This study is aimed at evaluating the factors affecting the success of semirigid ureterorenoscopy (URS) using the decision tree method. Materials and Methods: From January 2005 to November 2015, the data of consecutive patients treated for proximal ureteral stone were retrospectively analyzed. A total of 920 patients with proximal ureteral stone treated with semirigid URS were included in the study. All statistically significant attributes were tested using the decision tree method. Results: The model created using decision tree had a sensitivity of 0.993 and an accuracy of 0.857. While URS treatment was successful in 752 patients (81.7%), it was unsuccessful in 168 patients (18.3%). According to the decision tree method, the most important factor affecting the success of URS is whether the stone is impacted to the ureteral wall. The second most important factor affecting treatment was intramural stricture requiring dilatation if the stone is impacted, and the size of the stone if not impacted. Conclusions: Our study suggests that the impacted stone, intramural stricture requiring dilatation and stone size may have a significant effect on the success rate of semirigid URS for proximal ureteral stone. Further studies with population-based and longitudinal design should be conducted to confirm this finding. (C) 2017 S. Karger AG, Basel