The significance of machine learning in neonatal screening for inherited metabolic diseases.

ABSTRACT:

Background

Neonatal screening for inherited metabolic diseases (IMDs) has been revolutionized by tandem mass spectrometry (MS/MS). This study aimed to enhance neonatal screening for IMDs using machine learning (ML) techniques.

Methods

The study involved the analysis of a comprehensive dataset comprising 309,102 neonatal screening records collected in the Ningbo region, China. An advanced ML system model, encompassing nine distinct algorithms, was employed for the purpose of predicting the presence of 31 different IMDs. The model was compared with traditional cutoff schemes to assess its diagnostic efficacy. Additionally, 180 suspected positive cases underwent further evaluation.

Results

The ML system exhibited a significantly reduced positive rate, from 1.17% to 0.33%

SUBMITTER: Yang X 

PROVIDER: S-EPMC10993727 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature