Predictive modeling for early detection of biliary atresia in infants with cholestasis: Insights from a machine learning study

Cholestasis, characterized by the obstruction of bile flow, poses a significant concern in neonates and infants. It can result in jaundice, inadequate weight gain, and liver dysfunction. However, distinguishing between biliary atresia (BA) and non-biliary atresia in these young patients presenting w...

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Bibliographic Details
Published in:Computers in biology and medicine 2024-05, Vol.174, p.108439-108439, Article 108439
Main Authors: Chen, Xuting, Zhao, Dongying, Ji, Haochen, Chen, Yihuan, Li, Yahui, Zuo, Zongyu
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Biliary atresia
Biliary Atresia - diagnosis
Biomarkers
Children
Cholestasis
Decision making
Deep learning
Early Diagnosis
Efficiency
Feature selection
Female
Gallbladder
Gallbladder diseases
Hospitals
Humans
Infant
Infant, Newborn
Infants
Jaundice
Learning algorithms
Liver diseases
Machine Learning
Male
Medical personnel
Medical prognosis
Medical research
Model accuracy
Multi-verse optimizer
Neonates
Nomograms
Optimization algorithms
Prediction models
Support Vector Machine
Support vector machines
Water flow
γ-Glutamyltransferase
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Summary:Cholestasis, characterized by the obstruction of bile flow, poses a significant concern in neonates and infants. It can result in jaundice, inadequate weight gain, and liver dysfunction. However, distinguishing between biliary atresia (BA) and non-biliary atresia in these young patients presenting with cholestasis poses a formidable challenge, given the similarity in their clinical manifestations. To this end, our study endeavors to construct a screening model aimed at prognosticating outcomes in cases of BA. Within this study, we introduce a wrapper feature selection model denoted as bWFMVO-SVM-FS, which amalgamates the water flow-based multi-verse optimizer (WFMVO) and support vector machine (SVM) technology. Initially, WFMVO is benchmarked against eleven state-of-the-art algorithms, with its efficiency in searching for optimized feature subsets within the model validated on IEEE CEC 2017 and IEEE CEC 2022 benchmark functions. Subsequently, the developed bWFMVO-SVM-FS model is employed to analyze a cohort of 870 consecutively registered cases of neonates and infants with cholestasis (diagnosed as either BA or non-BA) from Xinhua Hospital and Shanghai Children's Hospital, both affiliated with Shanghai Jiao Tong University. The results underscore the remarkable predictive capacity of the model, achieving an accuracy of 92.639 % and specificity of 88.865 %. Gamma-glutamyl transferase, triangular cord sign, weight, abnormal gallbladder, and stool color emerge as highly correlated with early symptoms in BA infants. Furthermore, leveraging these five significant features enhances the interpretability of the machine learning model's performance outcomes for medical professionals, thereby facilitating more effective clinical decision-making. •This work proposed a water flow-based multi-verse optimization (MVO) algorithm called WFMVO, which incorporates water flow search into MVO, thereby enhancing both its search capability and efficiency.•We verified the excellent optimization capabilities of our method across two benchmark function sets. Specifically, WFMVO consistently exhibits superior performance compared to 11 other SOTA competitors.•By transforming WFMVO into its binary variant, bWFMVO, using a transfer function and integrating it with SVM to develop the bWFMVO-SVM-FS model, we achieved exceptional results in predicting biliary atresia.
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2024.108439