Using Machine Learning to Diagnose Autism Based on Eye Tracking Technology
<b>Background/Objectives:</b> One of the key challenges in autism is early diagnosis. Early diagnosis leads to early interventions that improve the condition and not worsen autism in the future. Currently, autism diagnoses are based on monitoring by a doctor or specialist after the child...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/1/66 |
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| author | Ameera S. Jaradat Mohammad Wedyan Saja Alomari Malek Mahmoud Barhoush |
| author_facet | Ameera S. Jaradat Mohammad Wedyan Saja Alomari Malek Mahmoud Barhoush |
| author_sort | Ameera S. Jaradat |
| collection | DOAJ |
| description | <b>Background/Objectives:</b> One of the key challenges in autism is early diagnosis. Early diagnosis leads to early interventions that improve the condition and not worsen autism in the future. Currently, autism diagnoses are based on monitoring by a doctor or specialist after the child reaches a certain age exceeding three years after the parents observe the child’s abnormal behavior. <b>Methods:</b> The paper aims to find another way to diagnose autism that is effective and earlier than traditional methods of diagnosis. Therefore, we used the Eye Gaze fixes map dataset and Eye Tracking Scanpath dataset (ETSDS) to diagnose Autistic Spectrum Disorder (ASDs), while a subset of the ETSDS was used to recognize autism scores. <b>Results:</b> The experimental results showed that the higher accuracy rate reached 96.1% and 98.0% for the hybrid model on Eye Gaze fixes map datasets and ETSDS, respectively. A higher accuracy rate was reached (98.1%) on the ETSDS used to recognize autism scores. Furthermore, the results showed the outperformer for the proposed method results compared to previous works. <b>Conclusions:</b> This confirms the effectiveness of using artificial intelligence techniques in diagnosing diseases in general and diagnosing autism, in addition to the need to increase research in the field of diagnosing diseases using advanced techniques. |
| format | Article |
| id | doaj-art-32b7966044b3447ea9f35fbb789cf9f7 |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-32b7966044b3447ea9f35fbb789cf9f72025-01-10T13:16:37ZengMDPI AGDiagnostics2075-44182024-12-011516610.3390/diagnostics15010066Using Machine Learning to Diagnose Autism Based on Eye Tracking TechnologyAmeera S. Jaradat0Mohammad Wedyan1Saja Alomari2Malek Mahmoud Barhoush3Computer Science Department, Yarmouk University, Irbid 21163, JordanComputer Science Department, Yarmouk University, Irbid 21163, JordanComputer Science Department, Yarmouk University, Irbid 21163, JordanComputer Science Department, Yarmouk University, Irbid 21163, Jordan<b>Background/Objectives:</b> One of the key challenges in autism is early diagnosis. Early diagnosis leads to early interventions that improve the condition and not worsen autism in the future. Currently, autism diagnoses are based on monitoring by a doctor or specialist after the child reaches a certain age exceeding three years after the parents observe the child’s abnormal behavior. <b>Methods:</b> The paper aims to find another way to diagnose autism that is effective and earlier than traditional methods of diagnosis. Therefore, we used the Eye Gaze fixes map dataset and Eye Tracking Scanpath dataset (ETSDS) to diagnose Autistic Spectrum Disorder (ASDs), while a subset of the ETSDS was used to recognize autism scores. <b>Results:</b> The experimental results showed that the higher accuracy rate reached 96.1% and 98.0% for the hybrid model on Eye Gaze fixes map datasets and ETSDS, respectively. A higher accuracy rate was reached (98.1%) on the ETSDS used to recognize autism scores. Furthermore, the results showed the outperformer for the proposed method results compared to previous works. <b>Conclusions:</b> This confirms the effectiveness of using artificial intelligence techniques in diagnosing diseases in general and diagnosing autism, in addition to the need to increase research in the field of diagnosing diseases using advanced techniques.https://www.mdpi.com/2075-4418/15/1/66image classificationASD diagnosisimage processingmachine learningdeep learninghybrid learning |
| spellingShingle | Ameera S. Jaradat Mohammad Wedyan Saja Alomari Malek Mahmoud Barhoush Using Machine Learning to Diagnose Autism Based on Eye Tracking Technology Diagnostics image classification ASD diagnosis image processing machine learning deep learning hybrid learning |
| title | Using Machine Learning to Diagnose Autism Based on Eye Tracking Technology |
| title_full | Using Machine Learning to Diagnose Autism Based on Eye Tracking Technology |
| title_fullStr | Using Machine Learning to Diagnose Autism Based on Eye Tracking Technology |
| title_full_unstemmed | Using Machine Learning to Diagnose Autism Based on Eye Tracking Technology |
| title_short | Using Machine Learning to Diagnose Autism Based on Eye Tracking Technology |
| title_sort | using machine learning to diagnose autism based on eye tracking technology |
| topic | image classification ASD diagnosis image processing machine learning deep learning hybrid learning |
| url | https://www.mdpi.com/2075-4418/15/1/66 |
| work_keys_str_mv | AT ameerasjaradat usingmachinelearningtodiagnoseautismbasedoneyetrackingtechnology AT mohammadwedyan usingmachinelearningtodiagnoseautismbasedoneyetrackingtechnology AT sajaalomari usingmachinelearningtodiagnoseautismbasedoneyetrackingtechnology AT malekmahmoudbarhoush usingmachinelearningtodiagnoseautismbasedoneyetrackingtechnology |