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  1. Ana Sayfa
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Yazar "Badicu, Georgian" seçeneğine göre listele

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    Estimation of Obesity Levels through the Proposed Predictive Approach Based on Physical Activity and Nutritional Habits
    (Mdpi, 2023) Gozukara Bag, Harika Gozde; Yagin, Fatma Hilal; Gormez, Yasin; Gonzalez, Pablo Prieto; Colak, Cemil; Gulu, Mehmet; Badicu, Georgian
    Obesity is the excessive accumulation of adipose tissue in the body that leads to health risks. The study aimed to classify obesity levels using a tree-based machine-learning approach considering physical activity and nutritional habits. Methods: The current study employed an observational design, collecting data from a public dataset via a web-based survey to assess eating habits and physical activity levels. The data included gender, age, height, weight, family history of being overweight, dietary patterns, physical activity frequency, and more. Data preprocessing involved addressing class imbalance using Synthetic Minority Over-sampling TEchnique-Nominal Continuous (SMOTE-NC) and feature selection using Recursive Feature Elimination (RFE). Three classification algorithms (logistic regression (LR), random forest (RF), and Extreme Gradient Boosting (XGBoost)) were used for obesity level prediction, and Bayesian optimization was employed for hyperparameter tuning. The performance of different models was evaluated using metrics such as accuracy, recall, precision, F1-score, area under the curve (AUC), and precision-recall curve. The LR model showed the best performance across most metrics, followed by RF and XGBoost. Feature selection improved the performance of LR and RF models, while XGBoost's performance was mixed. The study contributes to the understanding of obesity classification using machine-learning techniques based on physical activity and nutritional habits. The LR model demonstrated the most robust performance, and feature selection was shown to enhance model efficiency. The findings underscore the importance of considering both physical activity and nutritional habits in addressing the obesity epidemic.
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    Psychophysiological Adaptations to Exercise Training in COVID-19 Patients: A Systematic Review
    (Hindawi Ltd, 2024) AL-Mhanna, Sameer Badri; Batrakoulis, Alexios; Hofmeister, Martin; Drenowatz, Clemens; Ghazali, Wan Syaheedah Wan; Badicu, Georgian; Afolabi, Hafeez Abiola
    Introduction. Many COVID-19 patients display adverse symptoms, such as reduced physical ability, poor quality of life, and impaired pulmonary function. Therefore, this systematic review is aimed at evaluating the effectiveness of physical exercise on various psychophysiological indicators among COVID-19 patients who may be at any stage of their illness (i.e., critically ill, hospitalized, postdischarge, and recovering). Methods. A systematic search was conducted in PubMed, Scopus, ScienceDirect, Web of Science, and Google Scholar from 2019 to 2021. Twenty-seven studies, which assessed a total of 1525 patients, were included and analysed. Results. Overall, data revealed significant improvements in the following parameters: physical function, dyspnoea, pulmonary function, quality of life (QOL), lower limb endurance and strength, anxiety, depression, physical activity level, muscle strength, oxygen saturation, fatigue, C-reactive protein (CRP), interleukin 6 (IL-6), tumour necrosis factor-alpha (TNF-alpha), lymphocyte, leukocytes, and a fibrin degradation product (D-dimer). Conclusions. Physical training turns out to be an effective therapy that minimises the severity of COVID-19 in the intervention group compared to the standard treatment. Therefore, physical training could be incorporated into conventional treatment of COVID-19 patients. More randomized controlled studies with follow-up evaluations are required to evaluate the long-term advantages of physical training. Future research is essential to establish the optimal exercise intensity level and assess the musculoskeletal fitness of recovered COVID-19 patients. This trial is registered with CRD42021283087.
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    Using machine learning to determine the positions of professional soccer players in terms of biomechanical variables
    (Sage Publications Ltd, 2023) Yağın, Fatma Hilal; Hasan, Uday C. H.; Clemente, Filipe Manuel; Eken, Özgür; Badicu, Georgian; Gülü, Mehmet
    This study aimed to predict professional soccer players' positions with machine learning according to certain locomotor demands. Data from 20 male professional soccer players (five defenders, eight midfielders, and seven attackers) from the same team were tracked daily with a global navigation satellite system. A total of 1910 individual training sessions were recorded. The 10-fold cross-validation method was used. Soccer player positions were predicted using predictive models created with random forest (RF), gradient boosting tree, bagging classification, and regression trees algorithms, and the results were evaluated with comprehensive performance measures. Ratios and an importance plot were used to analyze the importance of the variables according to their contributions to the estimation. The findings show that the RF model achieved 100% accuracy, which means that RF can predict all player positions (100%). Running distance (26.5%), total distance (17.2%), and player load (15.8%) were the three variables that contributed the most to the estimation of the RF model and were the most important factor in distinguishing player positions. Consequently, our proposed machine learning approach (RF model) can reduce false alarms and player mispositioning.

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