Eur J Pediatr Surg 2021; 31(05): 414-419
DOI: 10.1055/s-0040-1715655
Original Article

Application of Machine Learning Techniques for Enuresis Prediction in Children

1  Department of Pediatric Surgery, School of Medicine, Eskisehir Osmangazi University, Eskisehir, Turkey
,
Mukaddes Baskaya
2  Department of Pediatric Nursing, Eskisehir Osmangazi University, Eskisehir, Turkey
,
Ozer Celik
3  Department of Mathematics–Computer, Eskisehir Osmangazi University, Eskisehir, Turkey
,
Fatih Cemrek
4  Department of Statistics, Eskisehir Osmangazi University, Eskisehir, Turkey
,
Ayfer Acikgoz
2  Department of Pediatric Nursing, Eskisehir Osmangazi University, Eskisehir, Turkey
› Author Affiliations

Abstract

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.



Publication History

Received: 18 February 2020

Accepted: 01 July 2020

Publication Date:
20 August 2020 (online)

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