• Madhubala Chaudhari (Patil)
• Dr. Rahat Khan

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide. Early detection is crucial for effective intervention and management. Recent advancements in machine learning, particularly the integration of Convolutional Neural Networks (CNN) and Support Vector Machines (SVM), have shown promise in enhancing diagnostic accuracy using neuroimaging data. This paper reviews the hybrid CNN-SVM approach, emphasizing its application in the Indian context. We discuss recent Indian research contributions, challenges, and future directions, highlighting the potential of these models to improve early AD detection and patient outcomes.

Early Detection of Alzheimer's Disease Using Hybrid CNN-SVM Models: A Machine Learning Approach

"Early Detection of Alzheimer's Disease Using Hybrid CNN-SVM Models: A Machine Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 8, page no.239-243, August-2025, Available :http://www.jetir.org/papers/JETIRHA06034.pdf

"Early Detection of Alzheimer's Disease Using Hybrid CNN-SVM Models: A Machine Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 8, page no. pp239-243, August-2025, Available at : http://www.jetir.org/papers/JETIRHA06034.pdf