Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Banu, P.K. Nizar; Azar, Ahmad Taher; Kamal, Nashwa Ahmad Title A name given to the resource Unveiling the root causes of diabetes using explainable AI Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Explainable AI in Healthcare Imaging for Medical Diagnoses: Digital Revolution of Artificial Intelligence;pp.365-387 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-23979-3.00015-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-23979-3.00015-4</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105011242189?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105011242189?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Banu P.K.N., Department of Computer Science, CHRIST (Deemed to be University) Central Campus, Karnataka, Bangalore, India; Azar A.T., College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia, Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt; Kamal N.A., Faculty of Engineering, Cairo University, Giza, Egypt Description An account of the resource Diabetes is a non-communicable wide spread disease across the world. To investigate the risky factors that are associated with diabetes, and to start early and customized treatment, researchers are fascinated to explore existing machine learning or deep learning models and to develop more reliable algorithms. The advancement in technology and the increase in world population is an enriching source to prompt and explore the factors that decide a person to be diabetic. Several algorithms and approaches are in place to address these factors but are lacking in emphasizing with more interpretable features which convinces patent to trust the medicine, treatment, and have meaningful conversation with the physicians and artificially intelligent systems. To encourage the participation of people with diabetes for customized treatment and considering societal needs, this chapter explores the possibility of Explainable Artificial Intelligence (XAI) in diabetes detection and figuring out the significant features that dominate diabetes. 2025 Elsevier Inc. All rights reserved. Subject The topic of the resource Classification; Diabetes; Machine learning; Random forest; SHAP; XAI Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044323979-3; 978-044323978-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online