Detecting Alzheimer’s Disease Using Deep Learning Framework for Medial IoT Application

More applications for wearable technology are being investigated and developed due to the significant technological growth of medical sensors and nanoelectronic devices. A new area of study is now possible because of wearable biomedical technology that has been merged with AI and ML. As it is used to track human behaviors and diagnose, this subject offers exciting potential. Neurodegenerative diseases (NDs) are becoming more prevalent in an aging society. The occurrence of a neurodegenerative illness takes place when the body’s central nervous system gradually deteriorates. Although this is rare, millions of people will be impacted worldwide. Despite the clinical importance of keeping an eye on ND’s symptoms, current practice makes it difficult since it is difficult to recall and describe symptoms effectively and because clinical sessions are infrequent. There are many neurodegenerative disorders among older people, such as Alzheimer’s diseases, Parkinson’s disease, and so on. So far, resting-state functional connectivity analysis has been followed to detect Alzheimer diseases (AD). Nevertheless, the Resting-state practical connectivity approach fails to take into account the distinctive features of different frequency bands, which encompass the brain’s most crucial atrophies. Hence, this work proposes an automatic Alzheimer disease detection algorithm based on their applications for various bands. Initially, the proposed detection algorithm has been learned using SVM and KNN to deal with AD disease. By adjusting different settings, we also explored additional machine learning and deep learning techniques and achieved high levels of accuracy. With only three bands, our suggested model performs well without external feature selection. The findings demonstrate that our approach is accurate (93.71%)/AUC (0.9363) in separating AD subjects from healthy controls.