Machine learning algorithms to the early diagnosis of fetal alcohol spectrum disorders.

ABSTRACT:

Introduction

Fetal alcohol spectrum disorders include a variety of physical and neurocognitive disorders caused by prenatal alcohol exposure. Although their overall prevalence is around 0.77%, FASD remains underdiagnosed and little known, partly due to the complexity of their diagnosis, which shares some symptoms with other pathologies such as autism spectrum, depression or hyperactivity disorders.

Methods

This study included 73 control and 158 patients diagnosed with FASD. Variables selected were based on IOM classification from 2016, including sociodemographic, clinical, and psychological characteristics. Statistical analysis included Kruskal-Wallis test for quantitative factors, Chi-square test for qualitative variables, and Machine Learning (ML) algorithms for prediction

SUBMITTER: Ramos-Triguero A 

PROVIDER: S-EPMC11131948 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature