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Obesity occurs as a result of excessive fat storage in the body and brings along physical and mental problems [1]. The physical function has been associated with impaired quality of life in various areas such as distress in society, sexual function, self-esteem, and work-related quality of life [2]. The prevalence of obesity has been steadily increasing over the past few decades and is now unprecedented. This increase has occurred in almost all ages, genders, and races. These data show that the segments of individuals in the highest weight categories i.e. (BMI> 40 kg / m2) increased proportionally more than those in the lower BMI categories (BMI <35 kg / m2) [3]. Given the numerous and important health consequences associated with obesity, there is an urgent need to develop highly effective interventions aimed at reversing these "obesogenic" drivers, including both government policies and health education and development programs. It is important to implement measures to be taken, including both government policies and health education and development programs, especially during the COVID-19 pandemic process we are in. In this study, the data set on the open-source access website was used for the prediction of obesity levels and consists of patient records of 17 variables created by the deep learning repository. In addition, the performance of deep learning methods in the prediction of obesity levels was examined and determined. Performance evaluation of models is compared in terms of accuracy, Fleiss's kappa, classification error, and absolute error.

Aims: Obesity is a health problem caused by excessive fat deposition in the body, which spreads rapidly, threatening the age group of children as well as adults. When it occurs in childhood, it probably continues in adulthood. This study aims to determine the factors related to overweight and obesity in primary and secondary school children in Mersin. Methods: This cross-sectional study was carried out with the children aged between five and 14 years in primary and secondary schools in Mersin, following the school health activities done by Mersin Public Health Directorate. Minimum sample size was calculated as 1.735 and questionnaires were sent to 2.000 people considering the variables of the class and school location. The independent variables of the study were sociodemographic characteristics, feeding habits, time allocated for physical and other activities, children's body mass index (BMI) categories according to the parents' declarations and parents' BMI categories according to their declarations. And, the dependent variable was the children's BMI values calculated by our measurements. The data of 1.980 students were analyzed. Results: The mean age of the study group was 9.28±2.53 (minimum-maximum: 5-14) years, and 50.9% of them were male. It was found that 14.6% of the group were obese and 21.5% were overweight. While 38.3% of boys were in the overweight or obese category, this rate was 33.8% for girls (p>0.05). The prevalence of overweight or obesity was significantly higher in secondary school students than in primary school students (p<0.05). Conclusions: The factors affecting childhood obesity identified in the studies conducted in Turkey have also become apparent in the current research we did on the school children aged 5-14 years in Mersin.

Breast cancer, which is an important public health problem worldwide, is one of the deadliest cancers in women. This study aims to classify open-access breast cancer data and identify important risk factors with the Stochastic Gradient Boosting Method. The open-access breast cancer dataset was used to construct a classification model in the study. Stochastic Gradient Boosting was used to classify the disease. Balanced accuracy, accuracy, sensitivity, specificity, and positive/negative predictive values were evaluated for model performance. The accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score metrics obtained with the Stochastic Gradient Boosting model were 100 %, 100 %, 100 %, 100 %, 100 %, and 100 %, and 100 % respectively. In addition, the importance of the variables obtained, the most important risk factors for breast cancer were a cave. points_mean, area_worst, and perimeter_worst, concave. points_worst respectively. According to the study results, with the machine-learning model Stochastic Gradient Boosting used, patients with and without breast cancer were classified with high accuracy, and the importance of the variables related to cancer status was determined. Factors with high variable importance can be considered potential risk factors associated with cancer status and can play an essential role in disease diagnosis.