@Article{cmc.2022.022663,
AUTHOR = {Anwer Mustafa Hilal, Imène ISSAOUI, Marwa Obayya, Fahd N. Al-Wesabi, Nadhem NEMRI, Manar Ahmed Hamza, Mesfer Al Duhayyim, Abu Sarwar Zamani},
TITLE = {Modeling of Explainable Artificial Intelligence for Biomedical Mental Disorder Diagnosis},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {71},
YEAR = {2022},
NUMBER = {2},
PAGES = {3853--3867},
URL = {http://www.techscience.com/cmc/v71n2/45831},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2022.022663}
}