Explainable Artificial Intelligence Multimodal of Autism Triage Levels Using Fuzzy Approach-Based Multi-criteria Decision-Making and LIME

A new framework for explainable artificial intelligence in the context of multimodal triage for autism spectrum disorders (ASD) using a fuzzy approach-based multi-criteria decision-making (MCDM) is proposed in this study. The framework consists of five phases. In the first phase, a real ASD dataset of 538 autistic patients is obtained and diagnosed based on 42 medical and sociodemographic criteria. In the second phase, an ASD methodology for triaging the 538 autistic patients into three levels (i.e., minor, moderate, and urgent) is presented using fuzzy approach-based MCDM techniques, namely, fuzzy Delphi method and fuzzy-weighted zero-inconsistency, followed by the processes for triaging autism patients. In the third phase, cost-sensitive learning is employed to balance two ASD datasets: one labeled based on the ASD triage methodology and the other labeled by specialized psychologists. Two multimodal of artificial intelligence are developed in the fourth phase using nine machine-learning algorithms for the balanced ASD datasets. The evaluation of the two multimodal setups is carried out using nine metrics. In the fifth phase, the local interpretable model-agnostic explanations (LIME) model is used to interpret the models using two scenarios. Four new algorithms are presented in the developed framework.