Machine Learning Prediction of Autism Spectrum Disorder From a Minimal Set of Medical and Background Information.

ABSTRACT:

Importance

Early identification of the likelihood of autism spectrum disorder (ASD) using minimal information is crucial for early diagnosis and intervention, which can affect developmental outcomes.

Objective

To develop and validate a machine learning (ML) model for predicting ASD using a minimal set of features from background and medical information and to evaluate the predictors and the utility of the ML model.

Design, setting, and participants

For this diagnostic study, a retrospective analysis of the Simons Foundation Powering Autism Research for Knowledge (SPARK) database, version 8 (released June 6, 2022), was conducted, including data from 30 660 participants after adjustments for missing values and class imbalances (15 330 with ASD and 15 330 without ASD). T

SUBMITTER: Rajagopalan SS 

PROVIDER: S-EPMC11333987 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature