Alzheimer’s Disease Prediction Using Deep Feature Extraction and Optimization

Farah Mohammad () and Saad Al Ahmadi
Additional contact information
Farah Mohammad: Center of Excellence and Information Assurance (CoEIA), King Saud University, Riyadh 11543, Saudi Arabia
Saad Al Ahmadi: Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia

Mathematics, 2023, vol. 11, issue 17, 1-17

Abstract: Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder that affects a substantial proportion of the population. The accurate and timely prediction of AD carries considerable importance in enhancing the diagnostic process and improved treatment. This study provides a thorough examination of AD prediction using the VGG19 deep learning model. The primary objective of this study is to investigate the effectiveness of feature fusion and optimization techniques in enhancing the accuracy of classification. The generation of a comprehensive feature map is achieved through the fusion of features that have been extracted from the fc7 and fc8 layers of VGG19. Several machine learning algorithms are employed to classify integrated features and recognize AD. The amalgamated feature map demonstrates a significant level of accuracy of 98% in the prognostication of AD, outperforming present cutting-edge methodologies. In this study, a methodology is utilized that makes use of the whale optimization algorithm (WoA), a metaheuristic approach to optimize features through feature selection. Feature optimization aims to eliminate redundant features and enhance the discriminatory power of the selected features. Following the optimization procedure, the F-KNN algorithm attained a precision level of 99%, surpassing the present state-of-the-art (SOTA) results reported in the current literature.

Keywords: Alzheimer’s disease; feature extraction; feature optimization; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/17/3712/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/17/3712/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:17:p:3712-:d:1227906

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().