Examinando por Autor "Yagin, Fatma Hilal"

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    Ítem
    Do oncologists recommend the “pill” of physical activity in their practice?: answers from the oncologist and patients’ perspectives
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024-04-28) Martínez de Aguirre Betolaza, Aitor; Dobaran Amezua, Ander; Yagin, Fatma Hilal; Cacicedo, Jon; Olasagasti Ibargoien, Jurgi; Castañeda Babarro, Arkaitz
    Objectives: The purposes of this current questionnaire-based study were to analyse whether oncologists prescribed PA to their patients in Spain, as well as the type of exercise recommended, the variables that influence whether or not to recommend it and to compare these recommendations with the values reported by their patients. Methods: Two online questionnaires were designed for this study. The first one, filled in by the oncologists (n = 93), contained aspects such as the attitude or barriers to promoting PA. The second was designed for patients with cancer (n = 149), which assessed PA levels and counselling received from oncologists, among other facets. Results: The majority of oncologists (97%) recommend PA during their consultations. Instead, only 62% of patients reported participating in exercise within the last 7 days. Walking was the most common form of exercise, reported by 50% of participants. Patients who received exercise recommendations from their oncologist walked for more days (p = 0.004; ES = 0.442) and more minutes per day (p = 0.022; ES = 0.410). The barriers most highlighted by patients were lack of time and not knowing how to perform PA. Conclusion: Oncologists and patients seem to be interested and able to participate in PA counselling and programmes. However, there was a discrepancy between what was reported by oncologists and expressed by patients in terms of recommendations for PA and the modality itself.
  • Miniatura
    Ítem
    Estimation of obesity levels with a trained neural network approach optimized by the Bayesian technique
    (MDPI, 2023-03-18) Yagin, Fatma Hilal ; Gülü, Mehmet; Gormez, Yasin; Castañeda Babarro, Arkaitz ; Colak, Cemil; Greco, Gianpero; Fischetti, Francesco; Cataldi, Stefania
    Background: Obesity, which causes physical and mental problems, is a global health problem with serious consequences. The prevalence of obesity is increasing steadily, and therefore, new research is needed that examines the influencing factors of obesity and how to predict the occurrence of the condition according to these factors. This study aimed to predict the level of obesity based on physical activity and eating habits using the trained neural network model. Methods: The chi-square, F-Classify, and mutual information classification algorithms were used to identify the most critical factors associated with obesity. The models’ performances were compared using a trained neural network with different feature sets. The hyperparameters of the models were optimized using Bayesian optimization techniques, which are faster and more effective than traditional techniques. Results: The results predicted the level of obesity with average accuracies of 93.06%, 89.04%, 90.32%, and 86.52% for all features using the neural network and for the features selected by the chi-square, F-Classify, and mutual information classification algorithms. The results showed that physical activity, alcohol consumption, use of technological devices, frequent consumption of high-calorie meals, and frequency of vegetable consumption were the most important factors affecting obesity. Conclusions: The F-Classify score algorithm identified the most essential features for obesity level estimation. Furthermore, physical activity and eating habits were the most critical factors for obesity prediction.