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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62778
Volume:
10 |
5 |
May 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
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.
Department Of Computer Science Engineering Kidangoor
College Of Engineering Kidangoor
Department Of Computer Science Engineering Kidangoor
College Of Engineering Kidangoor
Department Of Computer Science Engineering Kidangoor
College Of Engineering Kidangoor
Department Of Computer Science Engineering Kidangoor
College Of Engineering Kidangoor
Department Of Computer Science Engineering Kidangoor
College Of Engineering Kidangoor
Department Of Computer Science Engineering Kidangoor
College Of Engineering Kidangoor
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
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.
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Publication Date
5/7/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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