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

ABSTRACT:

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 selecti

SUBMITTER: Zhu ZX 

PROVIDER: S-EPMC11502559 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature