Data Mining for Autism Spectrum Disorder detection among Adults
نویسندگان
چکیده
Autism Spectrum Disorder (ASD) is one of the most common children's neurodevelopmental disorders (NDD) with an estimated global incidence 1% to 2%. There are two aims for this research, first, propose a data mining architecture that combines behavioural and clinical characteristics demographic data. Second, provide quick, acceptable easy way support ASD diagnosis. can be performed by conducting comparison study determine efficacy four possible classifiers: logistic regression (LR), sequential minimum optimization (SMO), naïve Bayes, instance-based technique based on k-neighbors (IBK). These classifiers have been Waikato Environment Knowledge Analysis (WEKA) tools distinguish autistic adults from healthy, normal subjects. The results showed that, 99.71%, SMO classification accuracy was 99.71, which exceeded other classifiers. proposed allows early detection ASD, distinguishing between healthy control This could help doctors clinicians giving them better idea what future holds people autism spectrum disorder improving therapy programs, allowing live long happy life.
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ژورنال
عنوان ژورنال: Al-Nahrain Journal for Engineering Sciences
سال: 2022
ISSN: ['2521-9162', '2521-9154']
DOI: https://doi.org/10.29194/njes.25040142