Deep learning-based alzheimer disease detection techniques

Nilanjana Pradhan; Shrdhha Sagar; Ajay Shankar Singh

doi:10.53730/ijhs.v6nS5.10270

Authors

Nilanjana Pradhan Galgotias University PHD Scholar Greater Noida, India
Shrdhha Sagar Galgotias University Professor Greater Noida, India
Ajay Shankar Singh Galgotias University Professor Greater Noida, India

Keywords:

Alzheimer disease (AD), Deep learning, Electronic health record (EHR), Single Nucleotide Polymorphisms (SNPs), Mild Cognitive Impairment (MCI)

Abstract

Alzheimer disease (AD) is one of the most common degenerative illnesses of the elderly worldwide. It is a progressive neurological condition that impairs cognitive memory. As a result, Alzheimer's sufferers struggle to recall daily activities, recollect family members, and solve logical problems. Medication which reduces the creation of proteins, block data communication between brain neurons and it can also delay the course of Alzheimer's disease. Mild Cognitive Impairment (MCI) seems to be a common disorder that does not usually progress to Alzheimer's. It is difficult to find patients with modest cognitive decline who may acquire Alzheimer's. As a result, creating deep learning-based disease detection techniques to assist clinicians in detecting prospective Alzheimer's patients is crucial. The performance comparison of the Imaging, Electronic Health Record (EHR), and Single nucleotide polymorphisms (SNP) datasets is evaluated using the metrics Accuracy, Sensitivity, Specificity, and Multi Area. Different mistakes are added under the curves for gradient calculation.

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