Ensemble Neuro Evolution of Augmenting Topologies Using Fused Features for Alzheimer’s Disease Diagnosis System
| International Journal of Electrical and Electronics Engineering |
| © 2023 by SSRG - IJEEE Journal |
| Volume 10 Issue 10 |
| Year of Publication : 2023 |
| Authors : S.Chithra, R.Vijayabhanu |
10.14445/23488379/IJEEE-V10I10P103
How to Cite?
S.Chithra, R.Vijayabhanu, "Ensemble Neuro Evolution of Augmenting Topologies Using Fused Features for Alzheimer’s Disease Diagnosis System," SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 10, pp. 20-31, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I10P103
Abstract:
Alzheimer’s Disease (AD) is a neurological condition that makes it difficult for a person to carry out the activities required of them daily. Because of the rapid advancement of Alzheimer’s patients and the lack of exact diagnostic techniques, early detection and classification of AD are essential research areas. One of the many researchers’ goals is to identify Alzheimer’s disease soon and correctly so it can be halted or delayed. Using a wide range of machine-learning algorithms, this study aims to compare the contemporary techniques for diagnosing and categorizing Alzheimer’s disease at the early stage. The proposed method effectively compares using the ADNI, which stands for the dataset, which is available to the public. Similarly, it reveals that the multi-feature combination methodology outperforms the single-feature extraction method. This paper proposes an AD diagnosis system that uses ML algorithms such as Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbour (KNN), Neuro Evolution of Augmenting Topologies (NEAT), and Bagging-NEAT (proposed) to diagnose AD in patients accurately. According to the study’s findings, the Bagging Neat can efficiently classify the stages of Alzheimer’s disease with an accuracy of 95.8% on the test dataset.
Keywords:
Alzheimer’s Disease, Features extraction, NEAT, Ensemble, Bagging, MR images.
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