Alzheimer’s disease diagnosis using genetic programming based on higher order spectra features

In Alzheimer’s diagnosis field, Computer-Aided Diagnosis (CADx) technology can improve the work performance of medical researchers and practitioners since it gives early chances to patient’s eligibility for clinical trials. The aim of this study is to develop a novel CADx system for the diagnosis of...

Full description

Saved in:
Bibliographic Details
Published in:Machine learning with applications Vol. 7; p. 100225
Main Authors: Nasrolahzadeh, Mahda, Rahnamayan, Shahryar, Haddadnia, Javad
Format: Journal Article
Language:English
Published: Elsevier Ltd 15.03.2022
Elsevier
Subjects:
ISSN:2666-8270, 2666-8270
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In Alzheimer’s diagnosis field, Computer-Aided Diagnosis (CADx) technology can improve the work performance of medical researchers and practitioners since it gives early chances to patient’s eligibility for clinical trials. The aim of this study is to develop a novel CADx system for the diagnosis of Alzheimer’s disease (AD) by utilizing genetic programming (GP) as data-driven evolutionary computation based modeling. The proposed method invokes a majority voting based scheme to select a set of most discriminant features which leads to the highest diagnosis accuracy of the final classification. The effectiveness of GP in categorizing patients with Alzheimer’s versus healthy group was revealed by developing models according to their performance in terms of higher-order spectra (HOS) features. The results show that the GP method achieved better performance compared to other the-state-of-the-art approaches. It is also found that the highest accuracy index was yielded by using the proposed data-driven modeling technique. The results of this study emphasize the practicality of GP-based method for developing CADx systems, on the basis of spontaneous speech analysis; can efficiently assist in the diagnosis of Alzheimer’s disease.
ISSN:2666-8270
2666-8270
DOI:10.1016/j.mlwa.2021.100225