A Machine Learning-based Analysis of Autism Prevalence Rates across US States against Multiple Potential Explanatory Variables

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Analysis of Autism Prevalence Rates across US States against Multiple Potential Explanatory Variables</title> <meta name="description" content="A Machine Learning-based Analysis of Autism Prevalence Rates across US States against Multiple Potential Explanatory Variables"> <meta name="keywords" content="Autism Spectrum Disorder, ASD, clustering, Machine Learning, predictive modeling."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="A Machine Learning-based Analysis of Autism Prevalence Rates across US States against Multiple Potential Explanatory Variables"> <meta name="citation_author" content="Ronit Chakraborty"> <meta name="citation_author" content="Sugata Banerji"> <meta name="citation_publication_date" content="2023/01/18"> <meta name="citation_journal_title" content="International Journal of Health and Medical Engineering"> <meta 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class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Ronit%20Chakraborty">Ronit Chakraborty</a>, <a href="https://publications.waset.org/search?q=Sugata%20Banerji"> Sugata Banerji</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>There has been a marked increase in the reported prevalence of Autism Spectrum Disorder (ASD) among children in the US over the past two decades. This research has analyzed the growth in state-level ASD prevalence against 45 different potentially explanatory factors including socio-economic, demographic, healthcare, public policy and political factors. The goal was to understand if these factors have adequate predictive power in modeling the differential growth in ASD prevalence across various states, and, if they do, which factors are the most influential. The key findings of this study include (1) there is a confirmation that the chosen feature set has considerable power in predicting the growth in ASD prevalence, (2) the most influential predictive factors are identified, (3) given the nature of the most influential predictive variables, an indication that a considerable portion of the reported ASD prevalence differentials across states could be attributable to over and under diagnosis, and (4) Florida is identified as a key outlier state pointing to a potential under-diagnosis of ASD.</p> <iframe src="https://publications.waset.org/10012908.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Autism%20Spectrum%20Disorder" title="Autism Spectrum Disorder">Autism Spectrum Disorder</a>, <a href="https://publications.waset.org/search?q=ASD" title=" ASD"> ASD</a>, <a href="https://publications.waset.org/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/search?q=Machine%0D%0ALearning" title=" Machine Learning"> Machine Learning</a>, <a href="https://publications.waset.org/search?q=predictive%20modeling." title=" predictive modeling."> predictive modeling.</a> </p> <a href="https://publications.waset.org/10012908/a-machine-learning-based-analysis-of-autism-prevalence-rates-across-us-states-against-multiple-potential-explanatory-variables" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10012908/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10012908/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10012908/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10012908/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10012908/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10012908/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10012908/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10012908/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10012908/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10012908/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10012908.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">672</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] “Autism and Developmental Disabilities Monitoring (ADDM) - Centers for Disease Control and Prevention,” https://www.cdc.gov/, accessed on Fri, November 11, 2022. <br>[Online]. Available: https://www.cdc.gov/ncbddd/autism/data/index.html <br>[2] C. Schweikert, Y. Li, D. Dayya, D. Yens, M. Torrents, and D. Hsu, “Analysis of autism prevalence and neurotoxins using combinatorial fusion and association rule mining,” 06 2009, pp. 400–404. <br>[3] R. Nataf, C. Skorupka, L. Amet, A. Lam, A. Springbett, and R. Lathe, “Porphyrinuria in childhood autistic disorder: Implications for environmental toxicity,” Toxicology and Applied Pharmacology, vol. 214, no. 2, 2006, pp. 99–108. <br>[4] R. A. Kumar, S. KaraMohamed, J. Sudi, D. F. Conrad, C. Brune, J. A. Badner, T. C. Gilliam, N. J. Nowak, J. Cook, Edwin H., W. B. Dobyns, and S. L. Christian, “Recurrent 16p11.2 microdeletions in autism,” Human Molecular Genetics, vol. 17, no. 4, 12 2007, pp. 628–638. <br>[5] L. A. Weiss, Y. Shen, J. M. Korn, D. E. Arking, D. T. Miller, R. Fossdal, E. Saemundsen, H. Stefansson, M. A. Ferreira, T. Green, O. S. Platt, D. M. Ruderfer, C. A. Walsh, D. Altshuler, A. Chakravarti, R. E. 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A Machine Learning-based Analysis of Autism Prevalence Rates across US States against Multiple Potential Explanatory Variables