Pregnancy risk factors in autism: a pilot study with artificial neural networks

BACKGROUND: Autism is a multifactorial condition in which a single risk factor can unlikely provide comprehensive explanation for the disease origin. Moreover, due to the complexity of risk factors interplay, traditional statistics is often unable to explain the core of the problem due to the strong inherent nonlinearity of relationships. The aim of this study was to assess the frequency of 27 potential risk factors related to pregnancy and peri-postnatal period. METHODS: The mothers of 45 autistic children and of 68 typical developing children completed a careful interview. Twenty-four siblings of 19 autistic children formed an internal control group. RESULTS: A higher prevalence of potential risk factors was observed in 22 and 15 factors in external control and internal control groups, respectively. For six of them, the difference in prevalence was statistically significant. Specialized artificial neural networks (ANNs) discriminated between autism and control subjects with 80.19% global accuracy when the dataset was preprocessed with TWIST (training with input selection and testing) system selecting 16 out of 27 variables. Logistic regression applied to 27 variables gave unsatisfactory results with global accuracy of 46%. CONCLUSION: Pregnancy factors play an important role in autism development. ANNs are able to build up a predictive model, which could represent the basis for a diagnostic screening tool.