A Machine Learning Approach to the Diagnosis of Autism Spectrum Disorder and Multi-Systemic Developmental Disorder Based on Retrospective Data and ADOS-2 Score

Abstract

Early and accurate diagnosis of autism spectrum disorders (ASD) and tailored therapeutic interventions can improve prognosis. ADOS-2 is a standardized test for ASD diagnosis. However, owing to ASD heterogeneity, the presence of false positives remains a challenge for clinicians. In this study, retrospective data from patients with ASD and multi-systemic developmental disorder (MSDD), a term used to describe children under the age of 3 with impaired communication but with strong emotional attachments, were tested by machine learning (ML) models to assess the best predictors of disease development as well as the items that best describe these two autism spectrum disorder presentations. Maternal and infant data as well as ADOS-2 score were included in different ML testing models. Depending on the outcome to be estimated, a best-performing model was selected. RIDGE regression model showed that the best predictors for ADOS social affect score were gut disturbances, EEG retrievals, and sleep problems. Linear Regression Model showed that term pregnancy, psychomotor development status, and gut disturbances were predicting at best for the ADOS Repetitive and Restricted Behavior score. The LASSO regression model showed that EEG retrievals, sleep disturbances, age at diagnosis, term pregnancy, weight at birth, gut disturbances, and neurological findings were the best predictors for the overall ADOS score. The CART classification and regression model showed that age at diagnosis and weight at birth best discriminate between ASD and MSDD.

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

“…ML has been effectively applied in the medical field to diagnose neurological disorders, including ASD (Vakadkar et al, 2021 ; Bahathiq et al, 2022 ; Briguglio et al, 2023 ) and ADHD (Slobodin et al, 2020 ; Mikolas et al, 2022 ; Briguglio et al, 2023 ; Kim et al, 2023 ). These studies have demonstrated the potential of ML to increase diagnostic accuracy, reduce time to diagnosis and improve reproducibility.…”

“…These studies have demonstrated the potential of ML to increase diagnostic accuracy, reduce time to diagnosis and improve reproducibility. For ASD, ML models have been used to identify key traits using sociodemographic, behavioral characteristics, or magnetic resonance imaging (MRI) results, thereby improving and automating the diagnostic process (Vakadkar et al, 2021 ; Bahathiq et al, 2022 ; Briguglio et al, 2023 ). Similarly, ML classifiers for ADHD have been developed based on clinical and psychological data (i.e.…”

Automated Detection of Autism Spectrum Disorder Symptoms using Text Mining and Machine Learning for Early Diagnosis

“…www.ijacsa.thesai.org Interview for Social and Communication Disorder (DISCO) [12]. However, due to the heterogeneity of ASD, the presence of false positive results remains a challenge for clinicians [13]. In addition, clinical examinations are time-consuming and negatively influence the patient behavior because patient is not in the home environment and is surrounded by unfamiliar people and these stimuli may be triggers for crisis-value behaviors.…”

Predicting Autism Spectrum Disorder: Transformer-Based Deep Learning Ensemble Framework Using Health Administrative & Birth Registry Data

“…However, these studies only considered a limited number of features. Machine learning (ML) has been used to consider a wide range of features for identifying ASD cases, including structural differences in brain magnetic resonance imaging (MRI) 14–16 , social and behavioural questionnaires 17–22 , and gene expression profiles 23,24 . These ML approaches, while promising, are not ethical or feasible when applied across a population.…”