Effects of data preprocessing on detecting autism in adults using web-based eye-tracking data.
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder, often associated with social and communication challenges and whose prevalence has increased significantly over the past two decades. The variety of different manifestations of ASD makes the condition difficult to diagnose, especially...
| Publicado en: | Behaviour & Information Technology Vol. 42; no. 14; pp. 2476 - 2485 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Nov2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173272250&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173272250 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0144929X B6Q jtl: Behaviour & Information Technology issn: 0144929X maglogo: Y pubinfo: dt: Nov2023 vid: 42 iid: 14 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 173272250 159396444 173272250 173272250 10.1080/0144929X.2022.2127376 173272250 ppf: 2476 ppct: 9 formats: tig: atl: Effects of data preprocessing on detecting autism in adults using web-based eye-tracking data. aug: au: Khalaji, Erfan Eraslan, Sukru Yesilada, Yeliz Yaneva, Victoria affil: Middle East Technical University, Northern Cyprus Campus, Mersin, Turkey sug: subj: Autism Spectrum Disorder Diagnosis Diagnosis, Computer Assisted Machine Learning Eye Movement Measurements Internet Searching Task Performance and Analysis Data Analytics Human Adult Middle Age Comparative Studies Algorithms World Wide Web Programming Languages Mann-Whitney U Test Descriptive Statistics Decision Trees Random Forest Adult: 19-44 years Middle Aged: 45-64 years ab: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder, often associated with social and communication challenges and whose prevalence has increased significantly over the past two decades. The variety of different manifestations of ASD makes the condition difficult to diagnose, especially in the case of highly independent adults. A large body of work is dedicated to developing new and improved diagnostic techniques, emphasising approaches that rely on objective markers. One such paradigm is investigating eye-tracking data as a promising and objective method to capture attention-related differences between people with and without autism. This study builds upon prior work in this area that focussed on developing a machine-learning classifier trained on gaze data from web-related tasks to detect ASD in adults. Using the same data, we show that a new data pre-processing approach, combined with an exploration of the performance of different classification algorithms, leads to an increased classification accuracy compared to prior work. The proposed approach to data pre-processing is stimulus-independent, suggesting that the improvements in performance shown in these experiments can potentially generalise over other studies that use eye-tracking data for predictive purposes. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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