Abilities and Disabilities—Applying Machine Learning to Disentangle the Role of Intelligence in Diagnosing Autism Spectrum Disorders

Abstract

ObjectiveAlthough autism spectrum disorder (ASD) is a relatively common, well-known but heterogeneous neuropsychiatric disorder, specific knowledge about characteristics of this heterogeneity is scarce. There is consensus that IQ contributes to this heterogeneity as well as complicates diagnostics and treatment planning. In this study, we assessed the accuracy of the Autism Diagnostic Observation Schedule (ADOS/2) in the whole and IQ-defined subsamples, and analyzed if the ADOS/2 accuracy may be increased by the application of machine learning (ML) algorithms that processed additional information including the IQ level.MethodsThe study included 1,084 individuals: 440 individuals with ASD (with a mean IQ level of 3.3 ± 1.5) and 644 individuals without ASD (with a mean IQ level of 3.2 ± 1.2). We applied and analyzed Random Forest (RF) and Decision Tree (DT) to the ADOS/2 data, compared their accuracy to ADOS/2 cutoff algorithms, and examined most relevant items to distinguish between ASD and Non-ASD. In sum, we included 49 individual features, independently of the applied ADOS module.ResultsIn DT analyses, we observed that for the decision ASD/Non-ASD, solely one to four items are sufficient to differentiate between groups with high accuracy. In addition, in sub-cohorts of individuals with (a) below (IQ level ≥4)/ID and (b) above average intelligence (IQ level ≤ 2), the ADOS/2 cutoff showed reduced accuracy. This reduced accuracy results in (a) a three times higher risk of false-positive diagnoses or (b) a 1.7 higher risk for false-negative diagnoses; both errors could be significantly decreased by the application of the alternative ML algorithms.ConclusionsUsing ML algorithms showed that a small set of ADOS/2 items could help clinicians to more accurately detect ASD in clinical practice across all IQ levels and to increase diagnostic accuracy especially in individuals with below and above average IQ level.

A machine learning-based diagnostic model for children with autism spectrum disorders complicated with intellectual disability

“…Currently, much of the past research on diagnostic models has focused on diagnostic imaging, neglecting the importance of demographic and behavioral observational data ( 40 ). Meanwhile, fewer studies have focused on early diagnosis and screening of ASD combined with ID ( 41 , 42 ). Furthermore, in the context of healthcare resource shortages and COVID-19 pandemic, the application of simple and effective diagnostic tools geared toward most primary care physicians can greatly reduce the burden on the healthcare system.…”

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

“…The last ML model attempted at performing a differential diagnosis between PDD and MSDD. This approach, making use of ML for purposes of differential diagnosis based on best-practice diagnostic instruments for autism, was already proposed with satisfying results, even though it was limited to a lower number of models from the ML perspective [ 39 ], focused on some specific clinical variables [ 40 ] or both [ 41 ]. In this respect, our work comparing different classification models showed that to the benefit of the clinician, the age at diagnosis and birth weight were seen to be the variables, which were the most predictive for distinguishing the two disorders.…”

Autism Spectrum Disorder and IQ – A Complex Interplay

“…Based on a previous finding, we further think that the influence of intelligence on existing and well established ASD diagnostic tools should be considered more explicitly. In this vein, we observed recently, that with respect to the cut-off exceedance of the Autism Diagnostic Observation Schedule-2 [ADOS-2 ( 41 )], individuals with below average IQ are significantly over classified (=false positives), while individuals with above average IQ are significantly under classified or misclassified (=false negatives) in regard to an ASD diagnosis [Wolff et al ( 42 )]. The misclassification of individuals with above average IQ leads to further questions.…”