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 Article Faculty Publications -Articles 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 Maju, Sonam Vayaliparambil; Siryapushpam, Gnana Prakasi Oliver Title A name given to the resource ApDeC: A rule generator for Alzheimer's disease prediction 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 IAES International Journal of Artificial Intelligence;Volume;14;Issue;3;pp.1772-1780 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.11591/ijai.v14.i3.pp1772-1780" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijai.v14.i3.pp1772-1780</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105008092478?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105008092478?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 Maju S.V., Department of Computer Science and Engineering, Christ University, Bangalore, India, School of Computer Science and Engineering, RV University, Bangalore, India; Siryapushpam G.P.O., Department of Computer Science and Engineering, Christ University, Bangalore, India Description An account of the resource Artificial intelligence (AI) paved the way and helping hand for the medical practitioners in various aspects and early disease prediction is one among many. Interdisciplinary research studies on the early prediction of diseases are often analyzed based on the accuracy of the prediction model. But how early these diseases can be predicted will not be answered in many of the research studies unless they have a time series data. This work proposes a machine learning model, ApDeC which solves the above-mentioned problem by generating association rules for the early disease prediction of Alzheimer patients. The ApDeC model calculates the probability of occurrence of eleven Alzheimer disease prediction risk factors and identifies the combination of diseases that can lead to Alzheimer disease. The association rules will be generated by considering the observed combination of risk factors. The research introduces an innovative approach that helps in the early prediction of Alzheimer disease from the risk factors/symptoms. The results show the strong correlation of diabetes and blood pressure with Alzheimer disease. 2025, Institute of Advanced Engineering and Science. All rights reserved. Subject The topic of the resource Alzheimers disease; Apriori algorithm; Artificial intelligence; Association rules; Machine learning Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science Relation A related resource ISSN: 20894872; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online