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    Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model
    (Mdpi, 2024) Aygun, Umran; Yagin, Fatma Hilal; Yagin, Burak; Yasar, Seyma; Colak, Cemil; Ozkan, Ahmet Selim; Ardigo, Luca Paolo
    This study aims to develop an interpretable prediction model based on explainable artificial intelligence to predict bacterial sepsis and discover important biomarkers. A total of 1572 adult patients, 560 of whom were sepsis positive and 1012 of whom were negative, who were admitted to the emergency department with suspicion of sepsis, were examined. We investigated the performance characteristics of sepsis biomarkers alone and in combination for confirmed sepsis diagnosis using Sepsis-3 criteria. Three different tree-based algorithms-Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost)-were used for sepsis prediction, and after examining comprehensive performance metrics, descriptions of the optimal model were obtained with the SHAP method. The XGBoost model achieved accuracy of 0.898 (0.868-0.929) and area under the ROC curve (AUC) of 0.940 (0.898-0.980) with a 95% confidence interval. The five biomarkers for predicting sepsis were age, respiratory rate, oxygen saturation, procalcitonin, and positive blood culture. SHAP results revealed that older age, higher respiratory rate, procalcitonin, neutrophil-lymphocyte count ratio, C-reactive protein, plaque, leukocyte particle concentration, as well as lower oxygen saturation, systolic blood pressure, and hemoglobin levels increased the risk of sepsis. As a result, the Explainable Artificial Intelligence (XAI)-based prediction model can guide clinicians in the early diagnosis and treatment of sepsis, providing more effective sepsis management and potentially reducing mortality rates and medical costs.
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    Cancer Metastasis Prediction and Genomic Biomarker Identification through Machine Learning and eXplainable Artificial Intelligence in Breast Cancer Research
    (Mdpi, 2023) Yagin, Burak; Yagin, Fatma Hilal; Colak, Cemil; Inceoglu, Feyza; Kadry, Seifedine; Kim, Jungeun
    Aim: Method: This research presents a model combining machine learning (ML) techniques and eXplainable artificial intelligence (XAI) to predict breast cancer (BC) metastasis and reveal important genomic biomarkers in metastasis patients. Method: A total of 98 primary BC samples was analyzed, comprising 34 samples from patients who developed distant metastases within a 5-year follow-up period and 44 samples from patients who remained disease-free for at least 5 years after diagnosis. Genomic data were then subjected to biostatistical analysis, followed by the application of the elastic net feature selection method. This technique identified a restricted number of genomic biomarkers associated with BC metastasis. A light gradient boosting machine (LightGBM), categorical boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Gradient Boosting Trees (GBT), and Ada boosting (AdaBoost) algorithms were utilized for prediction. To assess the models' predictive abilities, the accuracy, F1 score, precision, recall, area under the ROC curve (AUC), and Brier score were calculated as performance evaluation metrics. To promote interpretability and overcome the black box problem of ML models, a SHapley Additive exPlanations (SHAP) method was employed. Results: The LightGBM model outperformed other models, yielding remarkable accuracy of 96% and an AUC of 99.3%. In addition to biostatistical evaluation, in XAI-based SHAP results, increased expression levels of TSPYL5, ATP5E, CA9, NUP210, SLC37A1, ARIH1, PSMD7, UBQLN1, PRAME, and UBE2T (p <= 0.05) were found to be associated with an increased incidence of BC metastasis. Finally, decreased levels of expression of CACTIN, TGFB3, SCUBE2, ARL4D, OR1F1, ALDH4A1, PHF1, and CROCC (p <= 0.05) genes were also determined to increase the risk of metastasis in BC. Conclusion: The findings of this study may prevent disease progression and metastases and potentially improve clinical outcomes by recommending customized treatment approaches for BC patients.
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    Effect of listening to preferred music at different frequencies during warmup on physical performance and psychophysiological responses in male athletes (vol 15, 36120 , 2025)
    (Nature Portfolio, 2025) Jebabli, Nidhal; Boujabli, Manar; Ouergui, Ibrahim; Ouerghi, Nejmeddine; Yagin, Fatma Hilal; Yagin, Burak; Elkholi, Safaa M.
    [Abstract Not Available]
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    Prediction of obesity levels based on physical activity and eating habits with a machine learning model integrated with explainable artificial intelligence
    (Frontiers Media Sa, 2025) Gormez, Yasin; Yagin, Fatma Hilal; Yagin, Burak; Aygun, Yalin; Boke, Hulusi; Badicu, Georgian; Aghaei, Mohammadreza
    Objectives This study aims to build a machine learning (ML) prediction model integrated with explainable artificial intelligence (XAI) to categorize obesity levels from physical activity and dietary patterns. The inclusion of XAI methodologies facilitates a comprehensive understanding of the risk factors influencing the model predictions and thus increases transparency in the identification of obesity risk factors.Methods Six ML models were used: Bernoulli Naive Bayes, CatBoost, Decision Tree, Extra Trees Classifier, Histogram-based Gradient Boosting and Support Vector Machine. For each model, hyperparameters were tuned by random search methodology and model effectiveness was evaluated by repeated holdout testing. SHAP (SHapley Additive Annotations) and LIME (Local Interpretable Model Independent Annotations) interpretability methods were used to generate local and global feature importance measures.Results The CatBoost model exhibited the highest overall performance and achieved superior results in accuracy, precision, F1 score and AUC metrics. Nonetheless, other models such as Decision Tree and Histogram-based Gradient Boosting also yielded strong and competitive results. The results also highlighted age, weight, height and specific food patterns as key predictors of obesity. In terms of interpretability, LIME showed superior in fidelity, whereas SHAP showed improved sparsity and consistency across models, facilitating a comprehensive understanding of trait importance.Conclusion This research demonstrates that ML algorithms, when integrated with XAI technologies, can accurately predict obesity levels and explain important contributing risk factors. The use of SHAP and LIME increases model transparency, facilitating the identification of specific lifestyle patterns linked to obesity risk. These findings help to formulate more precise intervention techniques guided by a reliable and understandable predictive framework.
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    Serotoninergic modulation in the brainstem and hypothalamus of female overnourished rats: impact on mitochondrial markers, oxidative stress and BDNF mRNA levels
    (Frontiers Media Sa, 2025) Rodrigues, Thyago de Oliveira; dos Santos Junior, Osmar Henrique; Beltrao de Lemos, Maria Daniele Teixeira; de Sousa Fernandes, Matheus Santos; Yagin, Fatma Hilal; Yagin, Burak; Lagranha, Claudia J.
    Introduction Obesity is a global epidemic identified by the World Health Organization, and its complexity involves genetic, cultural, socioeconomic, and behavioral factors.Methods In this study, we used female Wistar rats, with litters standardized to nine female pups, which were divided into two groups: normally nourished or overnourished. The groups were further subdivided into control and fluoxetine-treated groups, with the pharmacological treatment maintained until the 21st day of life. At 30 days of age, euthanasia was performed, and tissues from the hypothalamus and brainstem were collected.Results We observed an increase in body weight and the Lee index in the overnourished group, but fluoxetine treatment reduced these indices. Additionally, overnourished rats consumed more palatable food. Biochemically, NADH content in the hypothalamus was altered by overnutrition but restored by fluoxetine treatment. Citrate synthase activity was reduced in the overnourished group in the hypothalamus but increased in the brainstem of fluoxetine-treated rats. The production of reactive oxygen species was higher in the overnourished group, and oxidative stress biomarkers showed increased levels of MDA and protein carbonylation in these rats. Overnutrition impaired the antioxidant activity of enzymes in both the hypothalamus and brainstem, whereas fluoxetine treatment improved this activity. BDNF expression was higher in the fluoxetine-treated groups compared to the overnourished group.Discussion These results demonstrate the detrimental effects of maternal overnutrition on the development of female offspring and the therapeutic potential of serotonergic manipulation to mitigate the early effects of obesity, with tissue-specific variations.