article · Journal of Disability Research
Autism spectrum disorder symptoms typically emerge in childhood and often continue throughout life. Although early diagnosis and intervention can substantially improve long-term outcomes, standard clinical diagnostic procedures remain costly and time-consuming. To address these challenges, computational approaches are increasingly investigated to assist conventional diagnostic workflows. This investigation evaluates several machine learning classification algorithms, including support vector machines, random forest, naïve Bayes, logistic regression, K-nearest neighbour, and decision trees, to construct predictive models for autism screening. The models were tested across publicly available datasets covering different age groups, specifically toddlers, children, adolescents, and adults, sourced from open repositories. The primary focus centres on identifying autism susceptibility in children at early stages to streamline the diagnostic pathway. Among the evaluated techniques, logistic regression achieved the highest predictive accuracy on the analysed dataset.
Early detection of autism spectrum disorder allows for prompt intervention, which substantially improves long-term outcomes for individuals. Standard clinical tests are often slow and costly, creating barriers to timely care. Demonstrating that accessible machine learning techniques can accurately detect autism indicators offers a pathway towards faster, more affordable screening processes across different age groups.
This work represents early-stage research focused on algorithms trained and tested on public non-clinical datasets. The findings could eventually enable software tools to assist clinicians and healthcare providers in faster screening and triage. However, real-world deployment would require extensive clinical validation, as current evidence is limited to retrospective evaluations on open repository data.
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Life symptoms associated with autism spectrum disorder (ASD) typically manifest during childhood and persist into adolescence and adulthood. ASD, which can be caused by genetic or environmental factors, can be significantly improved through early detection and treatment. Currently, standardized clinical tests are the primary diagnostic method for ASD. However, these tests are time consuming and expensive. Early detection and intervention are pivotal in enhancing the long-term prospects of children diagnosed with ASD. Machine-learning (ML) techniques are being utilized alongside conventional methods to improve the accuracy and efficiency of ASD diagnosis. Therefore, the paper aims to explore the feasibility of employing support vector machines, random forest classifier, naïve Bayes, logistic regression (LR), K-nearest neighbor, and decision tree classification models on our dataset to construct predictive models for predicting and analyzing ASD problems across different age groups: children, adolescents, and adults. The proposed techniques are assessed using publicly available nonclinical ASD datasets of three distinct datasets. The four ASD datasets, namely toddlers, adolescents, children, and adults, were obtained from publicly available repositories, specifically Kaggle and UCI ML. These repositories provide a valuable data source for research and analysis related to ASD. Our main objective is to identify the susceptibility to ASD in children during the early stages, thereby streamlining the diagnosis process. Based on our findings, LR demonstrated the highest accuracy for the selected dataset.
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DOI: 10.57197/jdr-2023-0064
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