Apriori-Backed Fuzzy Unification and Statistical Inference in Feature Reduction: An Application in Prognosis of Autism in Toddlers

Weak Artificial Intelligence (AI) allows the application of machine intelligence in modern health information technology to support medical professionals in bridging physical/psychological observations with clinical knowledge, thus generating diagnostic decisions. Autism, a highly variable neurodevelopmental condition marked by social impairments, reveals symptoms during infancy with no abatement with time due to comorbidities. There exist genetic, behavioral, neurological actors playing roles in the making of the disease and this constructs an ideal pattern recognition task. In this research, the Autism Screening Data (ASD) for toddlers was initially exploratorily analyzed to hypothesize impactful features which were further condensed and inferentially pruned. An interesting application of the business intelligence algorithm: Apriori has been made on transactions consisting of ten features and this has constituted a novel preprocessing step derived from market basket analysis. The huddling features were fuzzily modeled to a single feature, the membership function of which evaluated to the degree to which a toddler could be called autistic, thus paving the way to the first optimized Neural Network (NN). Features were further eliminated based on statistical t-tests and Chi-squared tests, administering features only with \(p{\text {-values}} < 0.05\)—giving rise to the second and final optimized model. The research showed that the unremitted 16-feature and the optimized 5-feature models showed equivalence in terms of maximum test accuracy: 99.68%, certainly with lower computation in the optimized scheme. The paper follows a ‘hard (EDA, inferential statistics) + soft (fuzzy logic) + hard (forward propagation) + soft (backpropagation)’ pipeline and similar systems can be used for similar prognostic problems.