A Machine Learning-Based Method for Autism Diagnosis Assistance in Children

Dutta, Sushama Rani; Giri, Soumyajit; Datta, Sujoy; Roy, Monideepa

doi:10.1109/icit.2017.26

Cited by 8 publications

(1 citation statement)

References 3 publications

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“…Some researchers are trying to develop practical ways to overcome the lack of trained autism specialists and the lengthy diagnostic process. Many research studies have used machine learning to optimize the traditional process of ASD diagnosis (Abbas et al, 2017; Dutta et al, 2017; Kosmicki et al, 2015). Liu et al (2016) utilized eye movement for ASD classification.…”

Section: Asd and Traditional Diagnostic Methodsmentioning

confidence: 99%

Autistic spectrum traits detection and early screening: A machine learning based eye movement study

Lin

et al. 2021

Child Adoles Psych Nursing

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Problem Autism spectrum disorders (ASD) are pervasive neurodevelopmental disorders and generally accompanied by social disorders, verbal or nonverbal communication defects, inability to concentrate and other negative symptoms that affect the autistic person's normal life. However, traditional screening methods are time‐consuming and public health resources are limited. Methods This study proposed a novel technique that combined eye‐movement data and machine learning algorithms for predicting autistic traits. We converted raw eye movement data into features, trained and tested a model for early screening. Findings In the preliminary experiment, 107 participants (average age = 24.84 ± 5.24 years) wore HTC Vive to watch a VR scene for 15–20 s. We explored eight classification models, among which the ensemble model performed best, with 0.73 accuracy, 0.68 precision, 0.81 recall, 0.74 F1‐score, and an area under the curve of 0.90. And in the test experiment, 22 participants (average age = 12.68 ± 7.61 years) diagnosed as ASD took the experiment and the ensemble model showed a recall of 0.77. Eye movement data is an effectively distinguishable tool and we find that the proportion of time to observe figure and animal region continuously can distinguish participants with obvious and unobvious autistic traits effectively in the model. Conclusion This study focuses on the detection of autistic traits, and proposes a more objective and faster method for undertaking early screening, which provides possibilities to save precious time to intervene and alleviate its symptoms before making a definite diagnosis.

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