The evaluation of urinary incontinence in secondary school children and risk factors: An epidemiological study.

AIM: Urinary incontinence is an important problem that can arise due to neurogenic or functional reasons and negatively affect the psychological, social and personality development of children. This study was conducted on secondary school students to determine the prevalence and risk factors of urinary incontinence at night and/or in the daytime. METHODS: The study universe included all secondary school students attending public elementary schools in the city centre of Eskisehir (N = 34 000). Ethics Committee and Provincial Directorate of National Education approvals were obtained before conducting the study. A data collection form prepared by the researchers and a consent form were delivered in a sealed envelope to the parents via the students. The study data were collected over the period 09 May 2018-30 May 2018. A total of 6957 questionnaires that were fully completed among the 7370 surveys were taken into consideration. The statistical analysis was carried out using the SPSS software package. RESULTS: The number of children found to have urinary incontinence was 215 (3.1%). It was seen that 33 children had urinary incontinence only in the daytime, 61 children experienced it both at night and during the day and 121 children at night. It was observed that 56% of the children suffering from urinary incontinence had not applied to any health facility for treatment prior to the study. It was found that among the risk factors for urinary incontinence were young age, late start of toilet training and presence of a family history of urinary incontinence. CONCLUSIONS: Children with urinary incontinence and their families need medical information and support to reach the root of the problem and seek solutions. Accompanying pathologies in detected cases can be determined in the early period by means of school screenings, and medical evaluation and support can prevent adverse effects on children's psychosocial and personality development.

Application of Machine Learning Techniques for Enuresis Prediction in Children.

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.