Innovative Method for Alzheimer Disease Prediction using GP-ELM-RNN

Yousef Methkal Abd Algani, S. Vidhya, Bhupaesh Ghai, Purnendu Bikash Acharjee, Mathur Nadarajan Kathiravan, Vijay Kumar Dwivedi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Brain illnesses are notoriously challenging because of their fragility, surgical complexity, and high treatment costs. Contrarily, it is not obligatory to carry out the operation, as the outcomes of the procedure may fall short of expectations. Adult-onset Alzheimer's disease, which causes memory loss and losing information to varied degrees, is one of the most common brain diseases. This will vary from person to person based on their current health situation. This highlights the need of using CT brain scans to classify the extent of memory loss and determine the patient's risk for Alzheimer's disease. The four main goals of Alzheimer's disease detection are preprocessing the data, extracting features, selecting features, and training the model with GP-ELM-RNN. The Replicator Neural Network has been utilized earlier for AD detection, however this study offers an improved version of the network, modified with ELM learning and the Garson algorithm. From this study, it is deduced that the proposed method is not only efficient, but also quite precise. In this research, GP-ELM-RNN network is built to four groups of images representing different stages of Alzheimer's disease: very mildly demented, mildly demented, averagely demented, and non-demented. The class of very mildly demented patients was found to have the highest accuracy (99.1%) and specificity (0.984%). As compared to the ELM and RNN models, this technique achieves superior accuracy (around 99.23%).

Original languageEnglish
Title of host publicationProceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages723-728
Number of pages6
ISBN (Electronic)9781665456302
DOIs
StatePublished - 2023
Externally publishedYes
Event2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023 - Salem, India
Duration: 4 May 20236 May 2023

Publication series

NameProceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

Conference

Conference2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023
Country/TerritoryIndia
CitySalem
Period4/05/236/05/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Alzheimer Disease (AD)
  • GP (Garson-Purned)
  • Grey Level Co-occurrence Matrix (GLCM)

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