The Potential of Hybrid Models in Alzheimer's Diagnosis: Combining Neural Networks and SVMs for Enhanced Accuracy

Abstract

Alzheimer’s Disease (AD) affects over 55 million people worldwide, posing a major challenge for healthcare systems as populations continue to age. As a progressive neurodegenerative disorder, AD leads to severe cognitive decline, memory loss, and a profound reduction in quality of life, ultimately resulting in death. The mortality rate for AD approaches 100%, with patients typically living only 3 to 11 years after diagnosis, underscoring the critical importance of early detection (Alzheimer’s Stages: How the Disease Progresses, n.d.). Despite extensive research, early diagnosis of AD remains difficult.