Benjamin D Zinszer, Joelle Hannon, Anqi Hu, Aya Élise Kouadio, Hermann Akpé, Fabrice Tanoh, Madeleine Wang, Zhenghan Qi, Kaja Jasińska
Statistical learning and children's emergent literacy in rural Côte d'Ivoire
Developmental science
subset artificial intelligence machine learning techniques mlts may evaluate large raw datasets study aim establish model mlt prediction enuresis children study included 8071 elementary school students total 704 children enuresis analysis data mlt another group including 704 nonenuretic children structured stratified sampling 34 independent variables 14 high feature values significantly affecting enuresis selected model estimation created training data fourteen independent variables order feature importance value starting age toilet training urinary urgency holding maneuvers prevent voiding frequency defecation history enuresis mother father childs room parents education level history enuresis siblings consanguineous marriage incomplete bladder emptying frequent voiding gender history urinary tract infection surgery past best mlt algorithm prediction enuresis determined logistic regression algorithm total accuracy rate model prediction 813 mlt might provide faster easier evaluation process studies enuresis large dataset model study may suggest selected variables high feature values could preferred priority screening studies enuresis mlt may prevent clinical errors due human cognitive biases may help physicians proactive diagnosis treatment enuresis