Affiliation:
1. Department of Electrical and Electronic Engineering, Final International University, Kyrenia, Turkey
Abstract
Alzheimer’s disease (AD) is the most prevalent brain disorder which affects millions of people worldwide. Early detection is crucial for possible treatment. In this regard, machine learning (ML) approaches are widely utilized for AD detection. In this paper, we propose an ML-based method that drastically reduces the dimensionality of features while maintaining the relevant features and boosting the overall performance. To remove irrelevant features, first statistical feature extraction method is applied, and then further reduction among remaining features is applied by utilizing the harmony search method (HSM). The selected features are the most informative features that are fed to the different classifiers. To test the effectiveness of the proposed method, we deployed three classification techniques including support vector machine (SVM), k-nearest neighbor (k-NN), and decision tree (DT). The experimental results show that the proposed method has a higher performance while decreasing the dimensionality of feature space. To guarantee that the performance of the proposed method is accurate, we applied an ensemble of three classifiers (SVM, KNN, and DT) for classification. The results of the proposed method verify that this method can be successfully deployed for AD detection, due to its high performance and low dimensional features, and can help improve the accuracy and efficiency of Alzheimer’s disease diagnosis. The proposed method demonstrated a significant improvement, achieving high performance in AD/HC classification, with accuracy, sensitivity, specificity, F1-score, MCC, and Cohen’s Kappa rates reaching 95.5%, 97%, 94%, 95.56%, 0.9104, and 0.9109, respectively. AD/HC classification displayed the highest performance. Additionally, in the more challenging pMCI/sMCI classification, the method achieved an accuracy of 78.50%, sensitivity of 84.00%, specificity of 73.00%, F1-score of 79.62%, MCC of 0.57, and Cohen’s Kappa of 0.59.
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
Reference33 articles.
1. Beata B.K. , Wojciech J. , Johannes K. , Piotr L. , Barbara M. , Alzheimer’s Disease— Biochemical and Psychological Background for Diagnosis and Treatment, Int J Mol Sci 24(2) (2023).
2. Alzheimer’s patients detection using support vector machine (SVM) with quantitative analysis;Sharma;Neurosci Informatics,2021
3. Deep learning reveals Alzheimer’s disease onset in MCI subjects: Results from an international challenge;Amoroso;J Neurosci Methods,2018
4. Neuropathological alterations in Alzheimer disease;Serrano-Pozo;Cold Spring Harb Perspect Med,2011
5. Detection of Early Stage Alzheimer’s Disease using EEG Relative Power with Deep Neural Network;Kim;Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS,2018