Early-Stage Detection of Alzheimer’s Disease Using Modified Firefly Based Ensemble Classification Model

International Journal of Computer Science and Engineering

© 2024 by SSRG - IJCSE Journal

Volume 11 Issue 11

Year of Publication : 2024

Authors : Sri Lakshmi, Sreenu Babu

10.14445/23488387/IJCSE-V11I11P102

How to Cite?

Sri Lakshmi, Sreenu Babu, "Early-Stage Detection of Alzheimer’s Disease Using Modified Firefly Based Ensemble Classification Model," SSRG International Journal of Computer Science and Engineering , vol. 11,  no. 11, pp. 11-19, 2024. Crossref, https://doi.org/10.14445/23488387/IJCSE-V11I11P102

Abstract:

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline affecting millions of people globally. Early detection is crucial to managing the disease effectively, enabling timely intervention to slow its progression. Most conventional machine learning approaches have been explored to enhance early prediction; however, these techniques often suffer from issues like overfitting, complex feature selection, and the inability to handle large datasets efficiently. To address the challenges of early AD prediction in the proposed model, the Hybrid Firefly optimized feature selection along with One Class SVM was used to achieve a better quality of Biomarker data and further integrate it with a customized ensemble classification model to improve the prediction rate. The proposed model performs better than conventional models and is measured in terms of accuracy, recall, precision and F1 score.

Keywords:

Modified Firefly algorithm, One Class SVM, Ensemble Classification, XGboost, Feature Selection.

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