Urinary incontinence is an important problem in childhood one that can occur for neurogenic or functional reasons. 1 According to the new terminology suggested by the International Child Continence Association, incontinence is divided into two groups-continuous and intermittent incontinence. While enuresis in the intermittent incontinence group only means incontinence during sleep, 2 the term "daytime incontinence" is used for children who do not have urinary incontinence during the night but have the condition during the

Introduction As a subset of artificial intelligence, machine learning techniques (MLTs) may evaluate very large and raw datasets. In this study, the aim is to establish a model by MLT for the prediction of enuresis in children. Materials and Methods The study included 8,071 elementary school students. A total of 704 children had enuresis. For analysis of data with MLT, another group including 704 nonenuretic children was structured with stratified sampling. Out of 34 independent variables, 14 with high feature values significantly affecting enuresis were selected. A model of estimation was created by training the data. Results Fourteen independent variables in order of feature importance value were starting age of toilet training, having urinary urgency, holding maneuvers to prevent voiding, frequency of defecation, history of enuresis in mother and father, having child's own room, parent's education level, history of enuresis in siblings, consanguineous marriage, incomplete bladder emptying, frequent voiding, gender, history of urinary tract infection, and surgery in the past. The best MLT algorithm for the prediction of enuresis was determined as logistic regression algorithm. The total accuracy rate of the model in prediction was 81.3%. Conclusion MLT might provide a faster and easier evaluation process for studies on enuresis with a large dataset. The model in this study may suggest that selected variables with high feature values could be preferred with priority in any screening studies for enuresis. MLT may prevent clinical errors due to human cognitive biases and may help the physicians to be proactive in diagnosis and treatment of enuresis.

Aim: Urinary incontinence is an important problem that can arise due to neurogenic or functional reasons and can negatively affect the psychological, social and personality development of children. This study was conducted in Eskişehir province, on secondary school students in order to determine the prevalence and nature of urinary incontinence at night and/or daytime. Methods: The study universe included all secondary school students attending state elementary schools in the city center of Eskişehir (N=34.000). Ethics Committee and Provincial Directorate of National Education approval was obtained before conducting the study, which was supported by Eskişehir Osmangazi University Scientific Research Projects Commission (2017-1876) . A data collection form prepared by the researchers, and a consent form were delivered in sealed envelope to the parents via the students. The study data were collected between 09.05.2018-30.05.2018. Only volunteers were included in the study. 6957 questionnaires which have been fully completed from the 7370 surveys have been taken into consideration. The statistical analysis was carried out using the SPSS soft ware package. Results: The number of children found to have urinary incontinence was determined to be 215 (3.1%). It has been determined that 33 children (0.5%) have urinary incontinence only at daytime, 61 children (0.9%) have urinary incontinence both at night and daytime, and 121 children (1.7%) have urinary incontinence only at night. It was observed that 56% of the children suffering from urinary incontinence had not applied to any health institution for treatment before. Conclusions: Children and families with urinary incontinence need medical information and support to cover the cause of the problem and suggestions for solutions. Accompanying pathologies in cases to be detected can be determined in the early period by means of school screenings and medical evaluation and support can prevent the psychosocial and personality development of children from being adversely affected.