[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123289-en":3,"doc-seo-123289-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123289,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","A Comparative Analysis of Machine Learning Models for Prediction of Autism Spectrum Disorder Using Screening Data","Autism spectrum disorder (ASD) is a neurodevelopmental condition that complicates interaction, communication, learning, and behavior. Accurate ASD prediction is challenging because diagnostic factors cannot rely on observation alone. This research conducts an in-depth comparison of multiple machine learning models for classifying autism traits using screening-related features and data, evaluating accuracy, precision, recall, specificity, F1 score, and AUC to determine the most effective approach for identifying ASD traits.","|  | Journal of Advanced Research in Applied Sciences and Engineering Technology\u003Cbr>Journal homepage:\u003Cbr>[https://semarakilmu.com. my/journals/index.php/applied_sciences_eng_tech/index](https://semarakilmu.com. my/journals/index.php/applied_sciences_eng_tech/index)\u003Cbr>ISSN: 2462-1943 |  |  |\n| --- | --- | --- | --- |\n| A Comparative Analysis of Machine Learning Models for Prediction of Autism Spectrum Disorder Using Screening Data\u003Cbr>Ming Yue Yeap1, Stephanie Chua1,*, Arif Bramantoro2\u003Cbr>1 Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak (UNIMAS), 94300 Kota Samarahan, Sarawak, Malaysia\u003Cbr>2 School of Computing and Informatics, Universiti Teknologi Brunei, Jalan Tungku Link, Gadong BE1410, Brunei Darussalam |  |  |  |\n| ARTICLE INFO |  | ABSTRACT |  |\n| Article history:\u003Cbr>Received 3 November 2023\u003Cbr>Received in revised form 18 September 2024 Accepted 4 October 2024\u003Cbr>Available online 18 November 2024 |  | Autism spectrum disorder (ASD) is a neurological and developmental disorder that affects how people interact with others, communicate, learn, and behave. ASD prediction is difficult because the diagnostic factors may not be based solely on observation. In this research paper, an in-depth comparative analysis of various machine learning models applied to the task of classifying autism traits was presented. Our study aimed to assess the performance of these models within the context of identifying individuals with autism based on relevant features and data. The machine learning models investigated in this study encompassed Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), Naive Bayes (NB), and Neural Network (NN) . The models were evaluated using six essential classification metrics: accuracy, precision, recall, specificity, F1 score, and AUC score. On the training dataset, our results reveal nuanced performance characteristics. SVM and RF excel in precision and recall, showing promise for accurate autism trait classification. KNN exhibits remarkable specificity, suggesting its potential for |  |\n| Keywords: |  | minimizing false positives. LR and NB demonstrate balanced performance across multiple metrics, while NN exhibits high precision and recall, albeit with higher |  |\n| Autism spectrum disorder; Classification; Machine learning; Prediction |  | computational demands. It was concluded that SVM was the best classification model for autism trait classification. |  |\n| 1. Introduction\u003Cbr>Autism spectrum disorder (ASD) is a disability in development caused by differences in the brain [1] . People with ASD usually have problems with limited or repetitive behaviours or interests, as well as communication skills and social engagement. Although the symptoms are easy to identify, a diagnosis of autism requires skilled medical professionals to supervise behavioural assessments that are measured according to the incidence of numerous symptoms that interfere with a person's capacity to talk, play, and create communication relationships. Depending on how serious the symptoms are, ASD can range from mild to severe [2] . |  |  |  |\n\n* Corresponding author.  \nE-mail address: [chlstephanie@unimas.my](chlstephanie@unimas.my)  \n[https://doi.org/10.37934/araset.53.1.175185](https://doi.org/10.37934/araset.53.1.175185)  \nParents who are concerned that their child may be autistic can bring them to medical practitioners. Medical practitioners will conduct screening tests on toddlers and children to diagnose if they have ASD. Many times, diagnosis cannot be determined in one visit, and it involves multiple visits to the clinic for some time, sometimes up to a few years to finally get a definitive diagnosis [3] . There are also teenagers and young adults who were not diagnosed with ASD from young and did not receive early intervention. The problem with ASD is that it is quite hard to diagnose as every child may progress through life at a different developme","cbCaicPYbWjqn6sr","https://ap.wps.com/l/cbCaicPYbWjqn6sr","pdf",2112122,1,11,"English","en",105,"# Article Info\n# Abstract\n# Keywords\n# 1. 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