Feature replacement methods enable reliable home video analysis for machine learning detection of autism.

ABSTRACT: Autism Spectrum Disorder is a neuropsychiatric condition affecting 53 million children worldwide and for which early diagnosis is critical to the outcome of behavior therapies. Machine learning applied to features manually extracted from readily accessible videos (e.g., from smartphones) has the potential to scale this diagnostic process. However, nearly unavoidable variability in video quality can lead to missing features that degrade algorithm performance. To manage this uncertainty, we evaluated the impact of missing values and feature imputation methods on two previously published autism detection classifiers, trained on standard-of-care instrument scoresheets and tested on ratings of 140 children videos from YouTube. We compare the baseline method of listwise deletion to classic univa

SUBMITTER: Leblanc E 

PROVIDER: S-EPMC7719177 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature