ApDeC: A rule generator for Alzheimer's disease prediction

Title

ApDeC: A rule generator for Alzheimer's disease prediction

Creator

Maju, Sonam Vayaliparambil; Siryapushpam, Gnana Prakasi Oliver

Description

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.

Source

IAES International Journal of Artificial Intelligence;Volume;14;Issue;3;pp.1772-1780

Date

01-01-2025

Publisher

Institute of Advanced Engineering and Science

Subject

Alzheimers disease; Apriori algorithm; Artificial intelligence; Association rules; Machine learning

Coverage

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

Rights

All Open Access; Gold Open Access

Relation

ISSN: 20894872;

Format

online

Language

English

Type

Article

Identifier

https://doi.org/10.11591/ijai.v14.i3.pp1772-1780

https://www.scopus.com/pages/publications/105008092478?origin=resultslist