Version 1
: Received: 2 September 2024 / Approved: 2 September 2024 / Online: 3 September 2024 (10:35:43 CEST)
How to cite:
Sarma, M.; Chatterjee, S. Biomarkers of ‘Late Onset Alzheimer’s Disease ‘, Diagnosis and Role of ’Deep Learning’. Preprints2024, 2024090154. https://doi.org/10.20944/preprints202409.0154.v1
Sarma, M.; Chatterjee, S. Biomarkers of ‘Late Onset Alzheimer’s Disease ‘, Diagnosis and Role of ’Deep Learning’. Preprints 2024, 2024090154. https://doi.org/10.20944/preprints202409.0154.v1
Sarma, M.; Chatterjee, S. Biomarkers of ‘Late Onset Alzheimer’s Disease ‘, Diagnosis and Role of ’Deep Learning’. Preprints2024, 2024090154. https://doi.org/10.20944/preprints202409.0154.v1
APA Style
Sarma, M., & Chatterjee, S. (2024). Biomarkers of ‘Late Onset Alzheimer’s Disease ‘, Diagnosis and Role of ’Deep Learning’. Preprints. https://doi.org/10.20944/preprints202409.0154.v1
Chicago/Turabian Style
Sarma, M. and Subarna Chatterjee. 2024 "Biomarkers of ‘Late Onset Alzheimer’s Disease ‘, Diagnosis and Role of ’Deep Learning’" Preprints. https://doi.org/10.20944/preprints202409.0154.v1
Abstract
Late-onset Alzheimer’s disease (LOAD) is a dementia category disease that starts after the age of 65. Progressive problems in thinking, behaviour, learning etc. are its characteristics. Due to its progressive nature, early diagnosis is crucial. A complex pathway mechanism with different risk factors is involved in LOAD progression. A comprehensive study is done on factors causing LOAD, biomarkers used for diagnosis in clinical and preclinical stage of the disease, ML (Machine Learning) techniques to identify disease stage. We reviewed research papers on clinical and preclinical diagnosis of LOAD using ML techniques from biomarker data: MRI, PET image, MMSE, Genome / Gene expression, DNA / RNA binding sites with protein, Epigenetic expression /DNA, methylation, Speech and text. We have listed improvement scope after finding the gaps and scope of using Generative AI and SMOTE (Synthetic Minority Oversampling Technique) for data augmentation. At the end, we have elaborated current use of DL (Deep Learning) and CNN (‘Convolution Neural Network’) in Alzheimer’s disease (AD) diagnosis.
Keywords
Alzheimer’s disease; Biomarker; Deep learning; Convolution neural network (CNN)
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
