Hidden Markov Models to Estimate the Probability of Having Autistic Children

Abstract

Genetic factors have been pointed out as the primary root associated with the risk of autism. Recent works indicate that approximately 80% of autistic people have inherited the condition from their parents. However, there are no estimates that indicate the likelihood of an autistic parent having an autistic child. Using Hidden Markov Models, together with the data of autism heritability, we developed a model to investigate the likelihood of autistic parents generating autistic children. Hidden Markov Models are a double-layered stochastic process, and it consists of a nonvisible stochastic process (not observable) that can be predicted through a visible one. Our model was built and validated using statistical data from the association of gender with recurrence of autism among siblings, as well as statistical data from the association of genetic factors with autism. Our results suggest that autistic parents may generate autistic children with probabilities of ≈ 33% for female children and ≈ 80% for male children. Such estimates could assist parents in some decision making processes according to the estimated risk of autism in their descendants.

Keywords

• Artificial intelligence
• autism spectrum disorder
• autism spectrum disorder heritability
• autism spectrum disorder prevalence
• computational intelligence
• hidden Markov models