Application of Machine Learning Techniques for Enuresis Prediction in Children

Machine learning analysis of 8,071 elementary school children identified 14 key predictive factors for enuresis, with toilet training age and urinary urgency being most significant. A logistic regression model achieved 81.3% accuracy in predicting enuresis, demonstrating potential for faster screening and reduced diagnostic bias in pediatric urology practice.