首页|Shanghai Jiao Tong University School of Medicine Reports Findings in Biliary Atr esia (Predictive modeling for early detection of biliary atresia in infants with cholestasis: Insights from a machine learning study)
Shanghai Jiao Tong University School of Medicine Reports Findings in Biliary Atr esia (Predictive modeling for early detection of biliary atresia in infants with cholestasis: Insights from a machine learning study)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Biliary Tract Diseases and Conditions - Biliary Atresia is the subjectof a report. According to news reporting originating in Shanghai, People’s Republic of China, by NewsRxjournal ists, research stated, “Cholestasis, characterized by the obstruction of bile fl ow, poses a significantconcern in neonates and infants. It can result in jaundi ce, inadequate weight gain, and liver dysfunction.”The news reporters obtained a quote from the research from the Shanghai Jiao Ton g University Schoolof Medicine, “However, distinguishing between biliary atresi a (BA) and non-biliary atresia in these youngpatients presenting with cholestas is poses a formidable challenge, given the similarity in their clinicalmanifest ations. To this end, our study endeavors to construct a screening model aimed at prognosticatingoutcomes in cases of BA. Within this study, we introduce a wrap per feature selection model denotedas bWFMVO-SVM-FS, which amalgamates the wate r flow-based multi-verse optimizer (WFMVO) andsupport vector machine (SVM) tech nology. Initially, WFMVO is benchmarked against eleven state-of-theartalgorith ms, with its efficiency in searching for optimized feature subsets within the mo del validated onIEEE CEC 2017 and IEEE CEC 2022 benchmark functions. Subsequent ly, the developed bWFMVO-SVMFSmodel is employed to analyze a cohort of 870 con secutively registered cases of neonates and infants withcholestasis (diagnosed as either BA or non-BA) from Xinhua Hospital and Shanghai Children’s Hospital,b oth affiliated with Shanghai Jiao Tong University. The results underscore the re markable predictivecapacity of the model, achieving an accuracy of 92.639 % and specificity of 88.865 %. Gamma-glutamyltransferase, triangular cord sign, weight, abnormal gallbladder, and stool color emerge as highly corre latedwith early symptoms in BA infants.”
ShanghaiPeople’s Republic of ChinaAsiaBile Duct Diseases and ConditionsBiliary AtresiaBiliary Tract Diseases and ConditionsCholestasisCongenital AbnormalitiesCyborgsDigestive System AbnormalitiesDigestive System Diseases and ConditionsEmerging TechnologiesGastroenterologyHealth and MedicineHospitalsMachine Learning
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