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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM62778

PREDICTAMIND - ALZHHEMIER'S DETECTION

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62778

Volume:

10 |

5 |

May 2026

Pages:

1-3

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM62778

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

SKS

Saran K S

Department Of Computer Science Engineering Kidangoor

College Of Engineering Kidangoor

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SKK

Shandry K K

Department Of Computer Science Engineering Kidangoor

College Of Engineering Kidangoor

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AS

Adith Shaji

Department Of Computer Science Engineering Kidangoor

College Of Engineering Kidangoor

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MR

Midhun Rajendran

Department Of Computer Science Engineering Kidangoor

College Of Engineering Kidangoor

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NM

Nikhil Mathew

Department Of Computer Science Engineering Kidangoor

College Of Engineering Kidangoor

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AK

Abhijith Krishnan

Department Of Computer Science Engineering Kidangoor

College Of Engineering Kidangoor

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Abstract

PredictaMind is an AI-powered system designed for the early detection of Alzheimer’s disease using a multimodal ap-proach. Alzheimer’s disease is a progressive neurological disorder that affects memory, thinking ability, and daily activities. Early detection is essential to slow disease progression and improve patient care. The proposed system integrates MRI brain image analysis with behavioral indicators such as eye blink patterns and typing dynamics. Eye blink and typing patterns help identify cognitive and motor decline, while MRI provides structural brain insights. Machine learning techniques are used to combine these features and generate accurate predictions. The system provides a non-invasive, cost-effective, and scalable solution for early Alzheimer’s detection.

KEYWORDS

Index Terms—Alzheimer’s Disease Machine Learning MRI Eye Blink Detection Typing Dynamics Multimodal System.

Cite This Article

Saran K S, Shandry K K, Adith Shaji, Midhun Rajendran, Nikhil Mathew, Abhijith Krishnan, "PREDICTAMIND - ALZHHEMIER'S DETECTION," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-3, May 2026. https://doi.org/10.55041/IJSREM62778

Full Article/Conclusion

PredictaMind is a multimodal AI-based system for early detection of Alzheimer’s disease. By combining MRI analysis with behavioral indicators such as eye blink patterns and typing dynamics, the system provides a more accurate and efficient diagnostic approach. The system reduces dependency on expensive medical pro-cedures and enables early-stage detection through non-invasive methods. Its modular design makes it scalable and suitable for real-world applications. Future improvements can include enhancing model accu-racy, adding more behavioral features, and integrating the system with healthcare platforms for continuous monitoring.

References

[1]    Y. AbdulAzeem, W. M. Bahgat, and M. Badawy, “A cnn based framework for classification of alzheimer’s disease,” Springer, vol. 33, no. 16, pp. 10 415–10 428, 2021. [2]    S. A. Ajagbe, K. A. Amuda, M. A. Oladipupo, O. F. Afe, and K. I. Okesola, “Multi-classification of alzheimer disease on magnetic resonance images (mri) using deep convolutional neural network approaches,” International Journal of Advanced Computer Research, vol. 11, no. 53, pp. 51–60, 2021. [3]    S. Dardouri, “An efficient method for early alzheimer’s disease detection based on mri images using deep convolutional neural networks,” Fron-tiers, vol. 8, p. 1563016, 2025. [4]    M. Kaya and Y. Cetin-Kaya, “A novel deep learning architecture opti-mization for multiclass classification of alzheimer’s disease level,” IEEE Access, vol. 12, pp. 46 562–46 584, 2024. [5]    G. M. u. d. Dar, A. Bhagat, S. I. Ansarullah, M. T. B. Othman, Y. Hamid, H. K. Alkahtani, I. Ullah, and H. Hamam, “A novel framework for classification of different alzheimer’s disease stages using cnn model,” MDPI, vol. 12, no. 2, p. 469, 2023. [6]    S. Tokushige, H. Matsumoto, S. Matsuda, S. Inomata-Terada, N. Kotsuki, M. Hamada, S. Tsuji, Y. Ugawa, and Y. Terao, “Early detection of cognitive decline in alzheimer’s disease using eye tracking,” Frontiers, vol. 15, p. 1123456, 2023. [7]    Q. Li, J. Yan, J. Ye, H. Lv, X. Zhang, Z. Tu, Y. Li, and Q. Guo, “Construction of a prediction model for alzheimer’s disease using an ai-driven eye-tracking task on mobile devices,” Springer, vol. 37, p. 9, 2025. [8]    E. Altinkaya, K. Polat, and B. Barakli, “Detection of alzheimer’s disease and dementia states based on deep learning from mri images: A com-prehensive review,” Journal of the Institute of Electronics and Computer, vol. 1, pp. 39–53, 2020.

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Publication Date

5/7/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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