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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM57110
Volume:
10 |
2 |
Feb 2026
Pages:
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.
Alzheimer’s disease is a progressive neurological disorder that impairs memory, cognition, and daily functioning, creating major challenges for patients, caregivers, and healthcare systems. Early detection and timely intervention are essential to slow disease progression and enhance quality of life. This study presents NeuroMind: Alzheimer’s Predictor & Care Navigator, an AI-powered healthcare platform designed to predict Alzheimer’s disease stages, provide personalized care guidance, and support patient–doctor connectivity. The system uses deep learning techniques, particularly convolutional neural networks (CNNs), to analyze MRI scans and accurately classify disease stages. In addition to prediction, the platform delivers customized insights on causes, management strategies, and treatment guidance. Key modules include disease stage prediction for accurate classification and personal insights, a doctor finder to locate nearby neurologists, a doctor management system for maintaining patient health records, and a PDF report generator for producing shareable diagnostic reports. The platform integrates a secure backend for data storage and a user-friendly interface for accessibility. Expected outcomes include high prediction accuracy, improved patient–doctor coordination, and enhanced caregiving support through data-driven recommendations. Overall, NeuroMind provides a comprehensive solution for early detection, personalized care, and effective management of Alzheimer’s disease.
KEYWORDS
Publication Date
1/2/2026
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.
Funding Information
No funding information available for this research.
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