The crucial problem when applying classification algorithms is unequal classes. An imbalanced dataset problem means, particularly in a two-class dataset, that the group variable of one class is comparatively more dominant than the group variable of the other class. The issue stems from the fact that the majority class dominates the minority class. The synthetic minority over-sampling technique (SMOTE) has been developed to deal with the classification of imbalanced datasets. SMOTE algorithm increases the number of samples by interpolating between the clustered minority samples. The SMOTE algorithm has three critical parameters, "k", "perc.over", and "perc.under". "perc.over" and "perc.under" hyperparameters allow determining the minority and majority class ratios. The "k" parameter is the number of nearest neighbors used to create new minority class instances. Finding the best parameter value in the SMOTE algorithm is complicated. A hybridized version of genetic algorithm (GA) and support vector machine (SVM) approaches was suggested to address this issue for selecting SMOTE algorithm parameters. Three scenarios were created. Scenario 1 shows the evaluation of support vector machine SVM) results without using the SMOTE algorithm. Scenario 2 shows that the SVM was used after applying SMOTE algorithm without the GA algorithm. In the third scenario, the results were analyzed using the SVM algorithm after selecting the SMOTE algorithm's optimization method. This study used two imbalanced datasets, drug use and simulation data. After, the results were compared with model performance metrics. When the model performance metrics results are examined, the results of the third scenario reach the highest performance. As a result of this study, it has been shown that a genetic algorithm can optimize class ratios and k hyperparameters to improve the performance of the SMOTE algorithm.

The postpartum period is characterized by significant changes in eating behaviors, emotional regulation and body weight. Intuitive eating and maternal attachment may play a key role in regulating women’s weight status during early postpartum. This cross-sectional study was conducted between February and May 2022 in Turkey, involving 264 healthy postpartum women (1–4 months) aged 18–40 years. Sociodemographic information, dietary habits, and anthropometric measurements were collected. Intuitive Eating Scale-2 (IES-2) and Maternal Attachment Scale (MAS) was administered. We classified BMI levels below 25 as “lower BMI”” and above 25 as “higher BMI”. Of the participants, 45.8% reported eating intuitively. Body mass index (BMI) was significantly higher in those with low levels of intuitive eating behaviour (p = < 0.001). A positive correlation was found between maternal attachment and intuitive eating (r = 0.129, p = 0.037). Higher intuitive eating was associated with lower BMI (β = -0.269, p = < 0.001). Skipping meals and night eating were associated with lower intuitive eating (p = 0.023). In logistic regression analysis, low intuitive eating (< 3.4 points) was associated with a high BMI during the early postpartum (OR = 2.63; 95% CI 1.57–4.40, p = < 0.001). Intuitive eating behaviour is positively associated with maternal attachment. However, higher intuitive eating is associated with lower BMI levels, night eating, and skipping meals. The results suggest that public health counselling promoting intuitive eating may be beneficial for early postpartum women who are at risk of excessive weight gain.

Background and Objectives: In clinical practice and research, it is necessary to use a standardized measurement tool to accurately determine dietary adherence in adults with celiac disease. The aim of this study is to validate the Celiac Dietary Adherence Test (CDAT) in the Turkish patients with celiac disease. Methods: The CDAT was translated using forward-backward translation method, reviewed by experts, and tested for comprehensibility. The final revised version of the CDAT was completed by 97 adults diagnosed with celiac disease and following a gluten-free diet. Construct validity was assessed using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Reliability was evaluated via internal consistency (Cronbach’s alpha) and test–retest reliability (Pearson correlation) after re-administration within 2 weeks. Results: Exploratory factor analysis yielded a Kaiser-Meyer-Olkin (KMO) value of 0.69, with Bartlett’s test of sphericity being significant (p < 0.001). EFA indicated a single-factor structure, explaining 55% of the total variance. CFA showed acceptable fit indices for the model (χ²/df = 1.45, CFI = 0.93, TLI = 0.90, RMSEA = 0.07, GFI = 0.94, AGFI = 0.88). Reliability analyses indicated Cronbach’s alpha of 0.70, and a test–retest correlation of 0.92. Items 5 and 6 were removed from the Turkish version of the CDAT because their factor loadings were below 0.40. Conclusions: The Turkish CDAT is valid and reliable for rapid, standardized assessment of adherence to a gluten-free diet in adults with celiac disease.

Background/Objectives: Sensory processing disorders (SID) are common in children with autism spectrum disorder (ASD) and can influence children’s eating behaviors. Evaluating the nutritional status of children with ASD is crucial for families or caregivers to manage their feeding. Therefore, this study aimed to compare the eating behaviors and dietary intake between children with ASD and children with ASD + SID. Methods: This cross-sectional study included 72 children with ASD aged 6–15 years, of whom 36 also had SID. Sociodemographic information and dietary habits of children were collected. The children’s body weight and height were measured. Children’s eating behaviors were assessed using the Children’s Eating Behavior Scale. Dietary intake was obtained using 3-day food consumption records. Results: The rate of overweight was higher in children with ASD compared to children with ASD + SID, but there was no statistically significant difference between the groups (p > 0.05). Children with ASD + SID were more likely to skip main meals than children with ASD (p < 0.05). Children with ASD + SID had significantly lower dietary reference intake levels of energy, macronutrients, fiber, PUFAs, vitamin E, B1, B6, folate, potassium, calcium, magnesium, phosphorus, and iron compared to children with ASD (p < 0.05). Increased scores on the drinking passion subscale were identified as a risk factor for SID (OR = 2.15, 95% CI [1.30, 4.30], p = 0.005). Conclusions: The higher frequency of skipping main meals, significantly lower energy and nutrient intake in the ASD + SID group indicates that these children are at higher nutritional risk. Incorporating sensory-based assessments and interventions into nutritional management may be crucial.