摘要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.