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Öğe Are there differences between Mediterranean diet and the consumption of harmful substances on quality of life?-an explanatory model in secondary education regarding gender(Frontiers Media Sa, 2023) Melguizo-Ibanez, Eduardo; Zurita-Ortega, Felix; Ubago-Jimenez, Jose Luis; Badicu, Georgian; Yagin, Fatma Hilal; Gonzalez-Valero, Gabriel; Ardigo, Luca PaoloBackgroundAdolescence is a key life stage in human development. It is during this stage of development that healthy and physical behaviors are acquired that will last into adulthood. Gender differences in the acquisition of these behaviors have been observed. This research aims to (a) study the levels of Mediterranean diet adherence, quality of life and alcohol and tobacco consumption as regarding the gender of the participants and (b) study the effects of the variable adherence to the Mediterranean diet, alcohol consumption and tobacco consumption on quality of life as a function of the gender of the participants.MethodsA non-experimental, cross-sectional, exploratory study was carried out in a sample of 1,057 Spanish adolescents (Average Age = 14.19; Standard Deviation = 2.87).ResultsThe comparative analysis shows that the male teenagers shows a higher Mediterranean diet adherence compared to the male adolescents (p <= 0.05) and a higher consumption of alcoholic beverages (p <= 0.05). On the contrary, adolescent girls show a higher consumption of alcoholic beverages than male participants (p <= 0.05). The exploratory analysis indicates that for boys, alcohol consumption has a beneficial effect on the quality of life of adolescents (beta = 0.904; p <= 0.001).ConclusionIn this case, participants show differences in the levels of Mediterranean diet adherence, consumption of harmful substances and quality of life according to gender. Likewise, there are different effects between the variables according to gender. Therefore, gender is a key factor to consider during adolescence.Öğe 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 PaoloThis 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.Öğe Combining the Strengths of the Explainable Boosting Machine and Metabolomics Approaches for Biomarker Discovery in Acute Myocardial Infarction(Mdpi, 2024) Arslan, Ahmet Kadir; Yagin, Fatma Hilal; Algarni, Abdulmohsen; AL-Hashem, Fahaid; Ardigo, Luca PaoloAcute Myocardial Infarction (AMI), a common disease that can have serious consequences, occurs when myocardial blood flow stops due to occlusion of the coronary artery. Early and accurate prediction of AMI is critical for rapid prognosis and improved patient outcomes. Metabolomics, the study of small molecules within biological systems, is an effective tool used to discover biomarkers associated with many diseases. This study intended to construct a predictive model for AMI utilizing metabolomics data and an explainable machine learning approach called Explainable Boosting Machines (EBM). The EBM model was trained on a dataset of 102 prognostic metabolites gathered from 99 individuals, including 34 healthy controls and 65 AMI patients. After a comprehensive data preprocessing, 21 metabolites were determined as the candidate predictors to predict AMI. The EBM model displayed satisfactory performance in predicting AMI, with various classification performance metrics. The model's predictions were based on the combined effects of individual metabolites and their interactions. In this context, the results obtained in two different EBM modeling, including both only individual metabolite features and their interaction effects, were discussed. The most important predictors included creatinine, nicotinamide, and isocitrate. These metabolites are involved in different biological activities, such as energy metabolism, DNA repair, and cellular signaling. The results demonstrate the potential of the combination of metabolomics and the EBM model in constructing reliable and interpretable prediction outputs for AMI. The discussed metabolite biomarkers may assist in early diagnosis, risk assessment, and personalized treatment methods for AMI patients. This study successfully developed a pipeline incorporating extensive data preprocessing and the EBM model to identify potential metabolite biomarkers for predicting AMI. The EBM model, with its ability to incorporate interaction terms, demonstrated satisfactory classification performance and revealed significant metabolite interactions that could be valuable in assessing AMI risk. However, the results obtained from this study should be validated with studies to be carried out in larger and well-defined samples.Öğe Effects of conditioning activities and time of day on male elite football players(Mre Press, 2023) Ben Maaouia, Ghazwa; Eken, Ozgur; Yagin, Fatma Hilal; Badicu, Georgian; Al-Mhanna, Sameer Badri; Ardigo, Luca Paolo; Souissi, NizarThis study evaluated the effects of different warm-up protocols based on conditioning activity combined with stretching exercises at different times of the day. Participants (20 first league of Tunisian football players) performed four warm-up protocols on two times a day in the morning: 09:00-10:00 and in the evening: 16:00-17:00, with at least 2 days between test sessions. All groups followed the warm-up randomly at two different periods of the day on non-consecutive days. The four protocols included: Dynamic stretching (DS), Dynamic stretching + conditioning activity (DS + High-Intensity Sprints HSJ), Dynamic stretching + drop jump (DS + DJ), and control (CONT). The thirty-meter sprint performance after different stretching and potentiation based warm-up protocols was recorded. Two-way Permutational multivariate analysis of variance (PERMANOVA) analysis was applied to examine the difference between warm-up protocols, the difference between the time of day and the interaction effect. The major finding revealed that 30 m sprint results and the exercise-induced temperature significantly differed from morning and evening stretching and potentiation-based warm-up protocols (statistically significant p < 0.05, and evening measurements were higher compared to the morning). In conclusion, and from a practical point of view, if the objective is to increase performance over a shorter period of time, each of these warm-up protocols can be useful. For the best improvement, DS + HSJ may be preferable both in the morning and the evening.Öğe Effects of short-term pre-competition weight loss on certain physiological parameters and strength change in elite boxers(Public Library Science, 2024) Yasul, Yavuz; Akcinar, Faruk; Yasul, Muhammet Enes; Kurtoglu, Ahmet; Eken, Ozgur; Badicu, Georgian; Ardigo, Luca PaoloBackground Athletes in certain sports aim to gain an advantage by competing in a lower body mass class instead of competing in their own body mass class. This study aims to reveal certain physiologic and strength changes in elite male boxers who lost body mass rapidly before the competition.Methods 30 thirty boxers who were aged between 19-24 years and having a mean age of 7.4 years participated in the study. To evaluate the effect of short-term dietary intake interventions on body composition and muscle strength before the competition, boxers were divided into three groups: control (C), exercise+diet1 (E+D1) and exercise+diet2 (E+D2) groups. The dietary habits of the participants were controlled and they participated in the training program. The data of the study consisted of variables such as body mass, height, regional muscle mass, body fat percentage, biceps and femur bicondylar circumference measurements before the competitions. Isometric strength measurements of knee extensors and flexors and shoulder internal and external rotators were also recorded.Results Physiologic parameters such as body mass change, BMI level, body fat percentage and leg muscle ratios of E+D2 were significantly decreased compared to C and E+D1 groups. Furthermore, submaximal and maximal strength production in knee extensors and flexors as well as shoulder internal and external rotators were significantly decreased in E+D2 compared to C and E+D1 groups.Conclusion The tendency to lose body mass quickly in a short of time may give the desired results in terms of BMI, body mass and fat percentage, but it may cause strength losses in boxers during the competition period.Öğe Effects of Swedish Massage at Different Times of the Day on Dynamic and Static Balance in Taekwondo Athletes(Mdpi, 2024) Bayrakdaroglu, Serdar; Eken, Ozgur; Bayer, Ramazan; Yagin, Fatma Hilal; Kizilet, Tuba; Kayhan, Recep Fatih; Ardigo, Luca PaoloThe purpose of this study is to investigate the impact of different durations of Swedish massage on the static and dynamic balance at different times of the day in taekwondo athletes. Twelve taekwondo athletes who had been practicing on a regular basis for more than 5 years participated in this study. Taekwondo athletes completed static and dynamic balance tests either after a no-massage protocol (NMP), a five-minute massage protocol (5MMP), a ten-minute massage protocol (10MMP), or a fifteen-minute massage protocol (15MMP) two times a day in the morning (08:00-12:00) and in the evening (16:00-20:00), on non-consecutive days. The findings of this study suggest that the duration of the massage has a discernible impact on dynamic balance, particularly with regard to the right foot. Taekwondo athletes who received a 10MMP or 15MMP displayed significantly improved dynamic balance compared to those in the NMP. Importantly, these improvements were independent of the time of day when the massages were administered. It underscores the potential benefits of incorporating short-duration Swedish massages into taekwondo athletes' pre-competition routines to enhance dynamic balance. These findings highlight the potential benefits of incorporating short-duration Swedish massages into taekwondo athletes' pre-competition routines to enhance dynamic balance, a critical component of their performance, regardless of the time of day.Öğe Explainable Artificial Intelligence Paves the Way in Precision Diagnostics and Biomarker Discovery for the Subclass of Diabetic Retinopathy in Type 2 Diabetics(Mdpi, 2023) Yagin, Fatma Hilal; Yasar, Seyma; Gormez, Yasin; Yagin, Burak; Pinar, Abdulvahap; Alkhateeb, Abedalrhman; Ardigo, Luca PaoloDiabetic retinopathy (DR), a common ocular microvascular complication of diabetes, contributes significantly to diabetes-related vision loss. This study addresses the imperative need for early diagnosis of DR and precise treatment strategies based on the explainable artificial intelligence (XAI) framework. The study integrated clinical, biochemical, and metabolomic biomarkers associated with the following classes: non-DR (NDR), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR) in type 2 diabetes (T2D) patients. To create machine learning (ML) models, 10% of the data was divided into validation sets and 90% into discovery sets. The validation dataset was used for hyperparameter optimization and feature selection stages, while the discovery dataset was used to measure the performance of the models. A 10-fold cross-validation technique was used to evaluate the performance of ML models. Biomarker discovery was performed using minimum redundancy maximum relevance (mRMR), Boruta, and explainable boosting machine (EBM). The predictive proposed framework compares the results of eXtreme Gradient Boosting (XGBoost), natural gradient boosting for probabilistic prediction (NGBoost), and EBM models in determining the DR subclass. The hyperparameters of the models were optimized using Bayesian optimization. Combining EBM feature selection with XGBoost, the optimal model achieved (91.25 +/- 1.88) % accuracy, (89.33 +/- 1.80) % precision, (91.24 +/- 1.67) % recall, (89.37 +/- 1.52) % F1-Score, and (97.00 +/- 0.25) % the area under the ROC curve (AUROC). According to the EBM explanation, the six most important biomarkers in determining the course of DR were tryptophan (Trp), phosphatidylcholine diacyl C42:2 (PC.aa.C42.2), butyrylcarnitine (C4), tyrosine (Tyr), hexadecanoyl carnitine (C16) and total dimethylarginine (DMA). The identified biomarkers may provide a better understanding of the progression of DR, paving the way for more precise and cost-effective diagnostic and treatment strategies.Öğe Exploring obesity, physical activity, and digital game addiction levels among adolescents: A study on machine learning-based prediction of digital game addiction(Frontiers Media Sa, 2023) Gulu, Mehmet; Yagin, Fatma Hilal; Gocer, Ishak; Yapici, Hakan; Ayyildiz, Erdem; Clemente, Filipe Manuel; Ardigo, Luca PaoloPrimary study aim was defining prevalence of obesity, physical activity levels, digital game addiction level in adolescents, to investigate gender differences, relationships between outcomes. Second aim was predicting game addiction based on anthropometric measurements, physical activity levels. Cross-sectional study design was implemented. Participants aged 9-14 living in Kirikkale were part of the study. The sample of the study consists of 405 adolescents, 231 girls (57%) and 174 boys (43%). Self-reported data were collected by questionnaire method from a random sample of 405 adolescent participants. To determine the physical activity levels of children, the Physical Activity Questionnaire for Older Children (PAQ-C). Digital Game addiction was evaluated with the digital game addiction (DGA) scale. Additionally, body mass index (BMI) status was calculated by measuring the height and body mass of the participants. Data analysis were performed using Python 3.9 software and SPSS 28.0 (IBM Corp., Armonk, NY, United States) package program. According to our findings, it was determined that digital game addiction has a negative relationship with physical activity level. It was determined that physical activity level had a negative relationship with BMI. In addition, increased physical activity level was found to reduce obesity and DGA. Game addiction levels of girl participants were significantly higher than boy participants, and game addiction was higher in those with obesity. With the prediction model obtained, it was determined that age, being girls, BMI and total physical activity (TPA) scores were predictors of game addiction. The results revealed that the increase in age and BMI increased the risk of DGA, and we found that women had a 2.59 times greater risk of DGA compared to men. More importantly, the findings of this study showed that physical activity was an important factor reducing DGA 1.51-fold. Our prediction model Logit (P) = 1/(1 + exp(-(-3.384 + Age*0.124 + Gender-boys*(-0.953) + BMI*0.145 + TPA*(-0.410)))). Regular physical activity should be encouraged, digital gaming hours can be limited to maintain ideal weight. Furthermore, adolescents should be encouraged to engage in physical activity to reduce digital game addiction level. As a contribution to the field, the findings of this study presented important results that may help in the prevention of adolescent game addiction.Öğe Hybrid Explainable Artificial Intelligence Models for Targeted Metabolomics Analysis of Diabetic Retinopathy(Mdpi, 2024) Yagin, Fatma Hilal; Colak, Cemil; Algarni, Abdulmohsen; Gormez, Yasin; Guldogan, Emek; Ardigo, Luca PaoloBackground: Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes mellitus, and early detection is crucial for effective management. Metabolomics profiling has emerged as a promising approach for identifying potential biomarkers associated with DR progression. This study aimed to develop a hybrid explainable artificial intelligence (XAI) model for targeted metabolomics analysis of patients with DR, utilizing a focused approach to identify specific metabolites exhibiting varying concentrations among individuals without DR (NDR), those with non-proliferative DR (NPDR), and individuals with proliferative DR (PDR) who have type 2 diabetes mellitus (T2DM). Methods: A total of 317 T2DM patients, including 143 NDR, 123 NPDR, and 51 PDR cases, were included in the study. Serum samples underwent targeted metabolomics analysis using liquid chromatography and mass spectrometry. Several machine learning models, including Support Vector Machines (SVC), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Multilayer Perceptrons (MLP), were implemented as solo models and in a two-stage ensemble hybrid approach. The models were trained and validated using 10-fold cross-validation. SHapley Additive exPlanations (SHAP) were employed to interpret the contributions of each feature to the model predictions. Statistical analyses were conducted using the Shapiro-Wilk test for normality, the Kruskal-Wallis H test for group differences, and the Mann-Whitney U test with Bonferroni correction for post-hoc comparisons. Results: The hybrid SVC + MLP model achieved the highest performance, with an accuracy of 89.58%, a precision of 87.18%, an F1-score of 88.20%, and an F-beta score of 87.55%. SHAP analysis revealed that glucose, glycine, and age were consistently important features across all DR classes, while creatinine and various phosphatidylcholines exhibited higher importance in the PDR class, suggesting their potential as biomarkers for severe DR. Conclusion: The hybrid XAI models, particularly the SVC + MLP ensemble, demonstrated superior performance in predicting DR progression compared to solo models. The application of SHAP facilitates the interpretation of feature importance, providing valuable insights into the metabolic and physiological markers associated with different stages of DR. These findings highlight the potential of hybrid XAI models combined with explainable techniques for early detection, targeted interventions, and personalized treatment strategies in DR management.Öğe Integrating proteomics and explainable artificial intelligence: a comprehensive analysis of protein biomarkers for endometrial cancer diagnosis and prognosis(Frontiers Media Sa, 2024) Yasar, Seyma; Yagin, Fatma Hilal; Melekoglu, Rauf; Ardigo, Luca PaoloEndometrial cancer, which is the most common gynaecological cancer in women after breast, colorectal and lung cancer, can be diagnosed at an early stage. The first aim of this study is to classify age, tumor grade, myometrial invasion and tumor size, which play an important role in the diagnosis and prognosis of endometrial cancer, with machine learning methods combined with explainable artificial intelligence. 20 endometrial cancer patients proteomic data obtained from tumor biopsies taken from different regions of EC tissue were used. The data obtained were then classified according to age, tumor size, tumor grade and myometrial invasion. Then, by using three different machine learning methods, explainable artificial intelligence was applied to the model that best classifies these groups and possible protein biomarkers that can be used in endometrial prognosis were evaluated. The optimal model for age classification was XGBoost with AUC (98.8%), for tumor grade classification was XGBoost with AUC (98.6%), for myometrial invasion classification was LightGBM with AUC (95.1%), and finally for tumor size classification was XGBoost with AUC (94.8%). By combining the optimal models and the SHAP approach, possible protein biomarkers and their expressions were obtained for classification. Finally, EWRS1 protein was found to be common in three groups (age, myometrial invasion, tumor size). This article's findings indicate that models have been developed that can accurately classify factors including age, tumor grade, and myometrial invasion all of which are critical for determining the prognosis of endometrial cancer as well as potential protein biomarkers associated with these factors. Furthermore, we were able to provide an analysis of how the quantities of the proteins suggested as biomarkers varied throughout the classes by combining the SHAP values with these ideal models.Öğe Mediterranean diet adherence on self-concept and anxiety as a function of weekly physical activity: an explanatory model in higher education(Frontiers Media Sa, 2023) Melguizo-Ibanez, Eduardo; Gonzalez-Valero, Gabriel; Badicu, Georgian; Yagin, Fatma Hilal; Alonso-Vargas, Jose Manuel; Ardigo, Luca Paolo; Puertas-Molero, PilarIntroductionScientific literature has now demonstrated the benefits of an active lifestyle for people's psychological health. Based on the above statement, the aim was to (a) evaluate and adjust a structural equation model containing the variables anxiety, self-concept, and Mediterranean diet adherence and (b) contrast the proposed theoretical model by studying the differences between the variables according to the level of weekly physical activity in a sample of 558 university students. MethodsA non-experimental, exploratory, cross-sectional investigation has been proposed. Instruments such as the PREDIMED Questionnaire, the Beck Anxiety Inventory, the International Physical Activity Questionnaire, and the Form 5 Self-Concept Questionnaire were used to collect data. Results and discussionThe results illustrate that students showing low adherence to the Mediterranean diet had higher levels of anxiety (M = 0.95) than those showing a high degree of adherence (M = 0.75). It is also observed that young people with a high degree of adherence to the Mediterranean diet report higher scores in the different dimensions of self-concept compared to young people with a low degree of adherence. In conclusion, it is affirmed that young people who show a high degree of adherence to this dietary pattern show lower levels of anxiety and greater recognition of the different areas of their self-concept.
