Data Mining for Autism Spectrum Disorder detection among Adults
Autism Spectrum Disorder (ASD) is one of the most common children's neurodevelopmental disorders (NDD) with an estimated global incidence of 1% to 2%. There are two aims for this research, first, to propose a data mining architecture that combines behavioural and clinical characteristics with...
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| Format: | Article |
| Language: | English |
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Al-Nahrain Journal for Engineering Sciences
2022-12-01
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| Series: | مجلة النهرين للعلوم الهندسية |
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| Online Access: | https://nahje.com/index.php/main/article/view/985 |
| _version_ | 1811166307488366592 |
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| author | Noor Al-Qazzaz Sumaya Jaffer Israa Abdulazez Teba Yousif |
| author_facet | Noor Al-Qazzaz Sumaya Jaffer Israa Abdulazez Teba Yousif |
| author_sort | Noor Al-Qazzaz |
| collection | DOAJ |
| description |
Autism Spectrum Disorder (ASD) is one of the most common children's neurodevelopmental disorders (NDD) with an estimated global incidence of 1% to 2%. There are two aims for this research, first, to propose a data mining architecture that combines behavioural and clinical characteristics with demographic data. Second, to provide a quick, acceptable and easy way to support the ASD diagnosis. this can be performed by conducting a comparison study to determine the efficacy of four possible classifiers: logistic regression (LR), sequential minimum optimization (SMO), naïve Bayes, and instance-based technique based on k-neighbors (IBK). These classifiers have been performed with Waikato Environment for Knowledge Analysis (WEKA) tools to distinguish autistic adults from healthy, normal subjects. The results showed that, with 99.71%, SMO classification accuracy was 99.71, which exceeded the accuracy of other classifiers. The proposed architecture allows for early detection of ASD, distinguishing between ASD and healthy control subjects. This study could help doctors and clinicians by giving them a better idea of what the future holds for people with autism spectrum disorder (ASD) and by improving therapy programs, allowing people with ASD to live a long and happy life.
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| first_indexed | 2024-04-10T15:50:15Z |
| format | Article |
| id | doaj.art-095ff246ce6f40eabd2cbec3114b1ccd |
| institution | Directory Open Access Journal |
| issn | 2521-9154 2521-9162 |
| language | English |
| last_indexed | 2024-04-10T15:50:15Z |
| publishDate | 2022-12-01 |
| publisher | Al-Nahrain Journal for Engineering Sciences |
| record_format | Article |
| series | مجلة النهرين للعلوم الهندسية |
| spelling | doaj.art-095ff246ce6f40eabd2cbec3114b1ccd2023-02-11T15:13:27ZengAl-Nahrain Journal for Engineering Sciencesمجلة النهرين للعلوم الهندسية2521-91542521-91622022-12-0125410.29194/NJES.25040142Data Mining for Autism Spectrum Disorder detection among AdultsNoor Al-Qazzaz0Sumaya Jaffer1Israa Abdulazez2Teba Yousif3Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of BaghdadErbil Technical Medical Institute, Erbil Polytechnic University, Erbil - IraqDepartment of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of BaghdadDepartment of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad Autism Spectrum Disorder (ASD) is one of the most common children's neurodevelopmental disorders (NDD) with an estimated global incidence of 1% to 2%. There are two aims for this research, first, to propose a data mining architecture that combines behavioural and clinical characteristics with demographic data. Second, to provide a quick, acceptable and easy way to support the ASD diagnosis. this can be performed by conducting a comparison study to determine the efficacy of four possible classifiers: logistic regression (LR), sequential minimum optimization (SMO), naïve Bayes, and instance-based technique based on k-neighbors (IBK). These classifiers have been performed with Waikato Environment for Knowledge Analysis (WEKA) tools to distinguish autistic adults from healthy, normal subjects. The results showed that, with 99.71%, SMO classification accuracy was 99.71, which exceeded the accuracy of other classifiers. The proposed architecture allows for early detection of ASD, distinguishing between ASD and healthy control subjects. This study could help doctors and clinicians by giving them a better idea of what the future holds for people with autism spectrum disorder (ASD) and by improving therapy programs, allowing people with ASD to live a long and happy life. https://nahje.com/index.php/main/article/view/985Autism Spectrum DisorderClassificationData MiningSMOTEWEKA |
| spellingShingle | Noor Al-Qazzaz Sumaya Jaffer Israa Abdulazez Teba Yousif Data Mining for Autism Spectrum Disorder detection among Adults مجلة النهرين للعلوم الهندسية Autism Spectrum Disorder Classification Data Mining SMOTE WEKA |
| title | Data Mining for Autism Spectrum Disorder detection among Adults |
| title_full | Data Mining for Autism Spectrum Disorder detection among Adults |
| title_fullStr | Data Mining for Autism Spectrum Disorder detection among Adults |
| title_full_unstemmed | Data Mining for Autism Spectrum Disorder detection among Adults |
| title_short | Data Mining for Autism Spectrum Disorder detection among Adults |
| title_sort | data mining for autism spectrum disorder detection among adults |
| topic | Autism Spectrum Disorder Classification Data Mining SMOTE WEKA |
| url | https://nahje.com/index.php/main/article/view/985 |
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