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...

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Vydáno v:Machine learning with applications Ročník 7; s. 100225
Hlavní autoři: Nasrolahzadeh, Mahda, Rahnamayan, Shahryar, Haddadnia, Javad
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.03.2022
Elsevier
Témata:
Alzheimer’s disease
Classification
Feature selection
Genetic programming
Machine learning
Spontaneous speech signal
Feature selection
Alzheimer’s disease
Spontaneous speech signal
Genetic programming
Classification
Machine learning
ISSN:2666-8270, 2666-8270
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Shrnutí: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