TY - JFULL AU - Ronit Chakraborty and Sugata Banerji PY - 2023/2/ TI - A Machine Learning-based Analysis of Autism Prevalence Rates across US States against Multiple Potential Explanatory Variables T2 - International Journal of Health and Medical Engineering SP - 11 EP - 16 VL - 17 SN - 1307-6892 UR - https://publications.waset.org/pdf/10012908 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 193, 2023 N2 - 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. ER -