Key result
Explainable AI precisely detects and classifies ASD using bacterial foraging optimization and deep belief networks.
Why the study?
Autism spectrum disorder needs to be identified and classified early to reduce health crisis, highlighting the need for explainable artificial intelligence to detect and classify it precisely.
Does the XAI-ASD model improve the detection and classification of Autism Spectral Disorder?
Does the XAI-ASD model improve the detection and classification of Autism Spectral Disorder?
The proposed XAI-ASD model utilizing BFO-based feature selection and WOA-optimized DBN aims to improve the early detection and classification of Autism Spectrum Disorder.
May support XAI for ASD screening research; leaves open clinical validation and utility.
The abundant existence of both structured and unstructured data and rapid advancement of statistical models stressed the importance of introducing Explainable Artificial Intelligence (XAI), a process that explains how prediction is done in AI models. Biomedical mental disorder, i.e., Autism Spectral Disorder (ASD) needs to be identified and classified at early stage itself in order to reduce health crisis. With this background, the current paper presents XAI-based ASD diagnosis (XAI-ASD) model to detect and classify ASD precisely. The proposed XAI-ASD technique involves the design of Bacterial Foraging Optimization (BFO)-based Feature Selection (FS) technique. In addition, Whale Optimization Algorithm (WOA) with Deep Belief Network (DBN) model is also applied for ASD classification process in which the hyperparameters of DBN model are optimally tuned with the help of WOA. In order to ensure a better ASD diagnostic outcome, a series of simulation process was conducted on ASD dataset.
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Hilal et al. (2021) studied Autism Spectral Disorder (ASD). XAI-ASD model was evaluated on ASD detection and classification. An Explainable Artificial Intelligence model utilizing Bacterial Foraging Optimization and a Deep Belief Network was developed for the precise detection and classification of Autism Spectrum Disorder.
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