ISSN2320-2882
Impact Factor9.17
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ESTD2013
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Paper Title
Hybrid ANN-Ensemble Model for Early Alzheimer's Detection
Authors
Sandhiya S , Chandru K , Dharshini S , Gopika B , Janani R
Keywords
Alzheimer's Disease, Early Detection, Artificial Neural Network (ANN), Ensemble Learning, Hybrid Model, Mild Cognitive Impairment (MCI), Machine Learning, Neuro imaging Analysis, Clinical Decision Support System, Predictive Modeling.
Abstract
Early detection of Alzheimer's disease (AD) is crucial for enabling timely intervention and improving patient outcomes. This project proposes a Hybrid ANN-Ensemble Model for Early Alzheimer's Disease Detection that integrates Artificial Neural Networks (ANN) with advanced ensemble learning techniques to enhance predictive accuracy and robustness. The proposed system utilizes multi-model input data, including neuro imaging features, cognitive assessment scores, and relevant clinical parameters, to perform comprehensive analysis and early-stage classification. The ensemble component is incorporated to reduce overfitting and improve generalization, while the ANN model captures complex nonlinear relationships within high-dimensional medical data. Advanced preprocessing techniques such as feature selection, normalization, and dimensionality reduction are applied to optimize model performance while maintaining computational efficiency. Model validation is performed using cross-validation techniques to ensure reliability and stability. Performance is evaluated using standard metrics, including accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Compared to conventional single-model approaches, the proposed hybrid framework demonstrates improved sensitivity in detecting Mild Cognitive Impairment (MCI), a critical precursor to Alzheimer's disease. The system functions as an intelligent decision-support tool, assisting clinicians in early diagnosis, risk assessment, and timely clinical intervention.
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IJCRT — journal style
"Hybrid ANN-Ensemble Model for Early Alzheimer's Detection", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.14, Issue 2, page no.e581-e589, February 2026, Available :https://ijcrt.org/papers/IJCRT2602539.pdf
APA — 7th edition
S, S., K, C., S, D., B, G., & R, J. (2026). Hybrid ANN-Ensemble Model for Early Alzheimer's Detection. International Journal of Creative Research Thoughts, 14(2), e581-e589. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602539
MLA — 9th edition
S, Sandhiya, et al. "Hybrid ANN-Ensemble Model for Early Alzheimer's Detection." International Journal of Creative Research Thoughts, vol. 14, no. 2, 2026, pp. e581-e589, https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602539.
Chicago — 17th, bibliography
S, Sandhiya, et al. "Hybrid ANN-Ensemble Model for Early Alzheimer's Detection." International Journal of Creative Research Thoughts 14, no. 2 (2026): e581-e589. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602539.
Harvard — author–date
S, S. et al. (2026) 'Hybrid ANN-Ensemble Model for Early Alzheimer's Detection', International Journal of Creative Research Thoughts, 14(2), pp. e581-e589. Available at: https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602539
IEEE — numbered reference
S. S, C. K, D. S, G. B and J. R, "Hybrid ANN-Ensemble Model for Early Alzheimer's Detection," IJCRT, vol. 14, no. 2, pp. e581-e589, Feb. 2026.
Vancouver — biomedical
S S, K C, S D, B G, R J. Hybrid ANN-Ensemble Model for Early Alzheimer's Detection. IJCRT. 2026 Feb;14(2):e581-e589.
AMA — 11th edition
S S, K C, S D, B G, R J. Hybrid ANN-Ensemble Model for Early Alzheimer's Detection. IJCRT. 2026;14(2):e581-e589. Accessed at https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602539
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRT2602539,
author = {Sandhiya S and Chandru K and Dharshini S and Gopika B and Janani R},
title = {Hybrid ANN-Ensemble Model for Early Alzheimer's Detection},
journal = {International Journal of Creative Research Thoughts},
volume = {14},
number = {2},
pages = {e581-e589},
year = {2026},
month = {February},
issn = {2320-2882},
url = {https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602539}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - S, Sandhiya
AU - K, Chandru
AU - S, Dharshini
AU - B, Gopika
AU - R, Janani
TI - Hybrid ANN-Ensemble Model for Early Alzheimer's Detection
T2 - International Journal of Creative Research Thoughts
JA - IJCRT
VL - 14
IS - 2
PY - 2026
SN - 2320-2882
UR - https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602539
SP - e581-e589
ER -
Issue
Volume 14 Issue 2,
February-2026
Pages : e581-e589
Article Information
- Published In
- International Journal of Creative Research Thoughts — Volume 14, Issue 2, February 2026
- Pages
- e581-e589
- Published
- February 2026
- Registration ID
- IJCRT_301734
- Published Paper ID
- IJCRT2602539
- Research Area
- Science and Technology
- Author Type
- Indian Author
- Location
- Coimbatore, Tamilnadu, India
- Peer Review
- Request Peer Review Report
- Licence
- CC BY 4.0 · Open Access
- ISSN & Impact Factor
- 2320-2882 · IF 9.17
- Downloads
- 163
- Permanent Link
- https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602539
- Published Paper PDF
- https://ijcrt.org/papers/IJCRT2602539.pdf
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International Journal of Creative Research Thoughts
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The authors would like to thank the reviewers and the editorial board of International Journal of Creative Research Thoughts for their careful reading and constructive comments, and all colleagues who supported the preparation of this manuscript.
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