Multimodal Classification of Cognitive States in Alzheimer's Disease

Alzheimer's disease is a neurodegenerative disease which affects the brain and causes memory loss. Today, this is the most affected disease in the world and has been a real struggle to survive. It is a condition which affects a large number of individuals globally, and it's important to diagnose it early and distinguish between different cognitive states such as AD, MCI, and NC. Personality changes, hallucinations, and difficulties speaking and walking may be symptoms as the illness worsens. It is important to ensure that a patient get treatment on time and management of the disease. The accuracy of AD classification can be greatly improved by incorporating different forms of data, including clinical, genetic, imaging and electroencephalogram(EEG) data, according to a recent study. One area of research involves using biomarkers to identify the disease early, before symptoms are evident. In this paper, we suggest a multimodal approach to identifying cognitive states in Alzheimer's disease. Recent research has demonstrated that multimodal classification techniques for AD diagnosis offer both unique advantages and drawbacks. In conclusion,our study highlights the importance of multimodal data in improving the accuracy of AD classification and provides a promising approach for early diagnosis and management of the disease, ultimately offering hope for better outcomes and quality of life for affected individuals.