首页|Improving the second-tier classification of methylmalonic acidemia patients using a machine learning ensemble method

Improving the second-tier classification of methylmalonic acidemia patients using a machine learning ensemble method

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Introduction Methylmalonic acidemia(MMA)is a disorder of autosomal recessive inheritance,with an estimated prevalence of 1∶50,000.First-tier clinical diagnostic tests often return many false positives[five false positive(FP):one true positive(TP)].In this work,our goal was to refine a classification model that can minimize the number of false positives,currently an unmet need in the upstream diagnostics of MMA.Methods We developed machine learning multivariable screening models for MMA with utility as a secondary-tier tool for false positives reduction.We utilized mass spectrometry-based features consisting of 11 amino acids and 31 carnitines derived from dried blood samples of neonatal patients,followed by additional ratio feature construction.Feature selection strategies(selection by filter,recursive feature elimination,and learned vector quantization)were used to determine the input set for evaluating the performance of 14 classification models to identify a candidate model set for an ensemble model development.Results Our work identified computational models that explore metabolic analytes to reduce the number of false positives without compromising sensitivity.The best results[area under the receiver operating characteristic curve(AUROC)of 97％,sensitivity of 92％,and specificity of 95％]were obtained utilizing an ensemble of the algorithms random forest,C5.0,sparse linear discriminant analysis,and autoencoder deep neural network stacked with the algorithm stochastic gradient boosting as the supervisor.The model achieved a good performance trade-off for a screening application with 6％false-positive rate(FPR)at 95％sensitivity,35％FPR at 99％sensitivity,and 39％FPR at 100％sensitivity.Conclusions The classification results and approach of this research can be utilized by clinicians globally,to improve the overall discovery of MMA in pediatric patients.The improved method,when adjusted to 100％precision,can be used to further inform the diagnostic process journey of MMA and help reduce the burden for patients and their families.

Machine learningMethylmalonic acidemiaSecond-tier testStacking

Zhi-Xing Zhu、Georgi Z.Genchev、Yan-Min Wang、Wei Ji、Yong-Yong Ren、Guo-Li Tian、Sira Sriswasdi、Hui Lu

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Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine,Center for Biomedical Informatics,Shanghai Children's Hospital,School of Medicine,Shanghai Jiao Tong University,Shanghai,China

Center of Excellence in Computational Molecular Biology,Faculty of Medicine,Chulalongkorn University,Bangkok,Thailand

Newborn Screening Center,Shanghai Children's Hospital,School of Medicine,Shanghai Jiao Tong University,Shanghai,China

SJTU-Yale Joint Center for Biostatistics and Data Science,National Center for Translational Medicine,Shanghai Jiao Tong University,Shanghai,China

Center for Artificial Intelligence in Medicine,Research Affairs,Faculty of Medicine,Chulalongkorn University,Bangkok,Thailand

State Key Laboratory of Microbial Metabolism,Joint International Research Laboratory of Metabolic &Developmental Sciences,Department of Bioinformatics and Bi

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2024

10.1007/s12519-023-00788-6

世界儿科杂志(英文版)

世界儿科杂志(英文版)

CSTPCD
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年,卷(期)：2024.20(10)