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Subtyping intractable functional constipation in children using clinical and laboratory data in a classification
Yi-Hsuan Huang1,2, Chenjia Xie3, Chih-Yi Chou4
1Department of Gastroenterology, Children's Hospital of Nanjing Medical University, Nanjing, China.
Insights
Machine learning accurately classifies pediatric intractable functional constipation (IFC) subtypes using questionnaires and hormone levels. The Light Gradient Boosting Machine (LGBM) model achieved 83.8% accuracy, aiding in early management of IFC in children.
Area of Science:
- Pediatric Gastroenterology
- Biomedical Informatics
- Machine Learning Applications
Background:
- Intractable functional constipation (IFC) in children leads to severe symptoms and reduced quality of life, persisting into adulthood.
- Early and efficient subtyping of IFC into normal transit constipation (NTC), outlet obstruction constipation (OOC), and slow transit constipation (STC) is crucial for effective management.
- Machine learning offers a promising approach for early IFC subtyping using validated questionnaires and serum gastrointestinal hormone concentrations.
Purpose of the Study:
- To evaluate the efficacy of supervised machine learning models in classifying pediatric IFC subtypes.
- To identify key clinical and biochemical variables for accurate IFC subtyping.
- To compare the performance of different machine learning models for IFC classification.
Main Methods:
- 101 children with IFC and 50 controls were analyzed.
- Supervised machine learning models (SVM, Random Forest, LGBM) were employed to classify IFC subtypes.
- Classification was based on symptom severity, quality of life, self-efficacy (questionnaires), and serum gastrointestinal hormone levels (ELISA).
- Model accuracy was validated against radiopaque marker results.
Main Results:
- The Light Gradient Boosting Machine (LGBM) model achieved the highest accuracy (83.8%) in classifying IFC subtypes.
- Significant variables for classification included stool frequency, PAC-QOL satisfaction, SEFCQ emotional self-efficacy, motilin, and vasoactive intestinal peptide serum concentrations.
- The LGBM model demonstrated high performance with precision (84.5%), recall (83.6%), F1-score (83.4%), and AUROC (0.89).
Conclusions:
- Machine learning models, particularly LGBM, can efficiently classify pediatric IFC subtypes using clinical data and serum hormone levels.
- This approach facilitates early and accurate diagnosis, improving the management of IFC in children.
- Machine learning serves as a valuable tool for personalized treatment strategies in pediatric functional constipation.
Background:
Children with intractable functional constipation (IFC) who are refractory to traditional pharmacological intervention develop severe symptoms that can persist even in adulthood, resulting in a substantial deterioration in their quality of life. In order to better manage IFC patients, efficient subtyping of IFC into its three subtypes, normal transit constipation (NTC), outlet obstruction constipation (OOC), and slow transit constipation (STC), at early stages is crucial. With advancements in technology, machine learning can classify IFC early through the use of validated questionnaires and the different serum concentrations of gastrointestinal motility-related hormones.
Method:
A hundred and one children with IFC and 50 controls were enrolled in this study. Three supervised machine-learning methods, support vector machine, random forest, and light gradient boosting machine (LGBM), were used to classify children with IFC into the three subtypes based on their symptom severity, self-efficacy, and quality of life which were quantified using certified questionnaires and their serum concentrations of the gastrointestinal hormones evaluated with enzyme-linked immunosorbent assay. The accuracy of machine learning subtyping was evaluated with respect to radiopaque markers.
Results:
Of 101 IFC patients, 37 had NTC, 49 had OOC, and 15 had STC. The variables significant for IFC subtype classification, according to SelectKBest, were stool frequency, the satisfaction domain of the Patient Assessment of Constipation Quality of Life questionnaire (PAC-QOL), the emotional self-efficacy for Functional Constipation questionnaire (SEFCQ), motilin serum concentration, and vasoactive intestinal peptide serum concentration. Among the three models, the LGBM model demonstrated an accuracy of 83.8%, a precision of 84.5%, a recall of 83.6%, a f1-score of 83.4%, and an area under the receiver operating characteristic curve (AUROC) of 0.89 in discriminating IFC subtypes.
Conclusion:
Using clinical characteristics measured by certified questionnaires and serum concentrations of the gastrointestinal hormones, machine learning can efficiently classify pediatric IFC into its three subtypes. Of the three models tested, the LGBM model is the most accurate model for the classification of IFC, with an accuracy of 83.8%, demonstrating that machine learning is an efficient tool for the management of IFC in children.
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