Alzheimer’s Disease Diagnosis Using Machine Learning: A Survey

Alzheimer’s Disease Diagnosis Using Machine Learning: A Survey

Alzheimer’s is a neurodegenerative disorder affecting the central nervous system and cognitive processes, explicitly impairing detailed mental analysis. Throughout this condition, the affected individual’s cognitive abilities to process and analyze information gradually deteriorate, resulting in men...

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Bibliographic Details
Published in:Applied sciences Vol. 13; no. 14; p. 8298
Main Authors: Dara, Omer Asghar, Lopez-Guede, Jose Manuel, Raheem, Hasan Issa, Rahebi, Javad, Zulueta, Ekaitz, Fernandez-Gamiz, Unai
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-07-2023
Subjects:
Alzheimer's disease
Alzheimer’s disease diagnosis
Artificial intelligence
Brain cancer
Brain research
Cognitive ability
Deep learning
Dementia
Development and progression
Diagnostic imaging
Electroencephalography
feature selection
Genetic testing
Intelligence
Machine learning
Magnetic fields
Magnetic resonance imaging
Medical diagnosis
Medical imaging equipment
Medical research
Medicine, Experimental
Older people
Proteins
Skills
Tomography
Zoning
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Summary:Alzheimer’s is a neurodegenerative disorder affecting the central nervous system and cognitive processes, explicitly impairing detailed mental analysis. Throughout this condition, the affected individual’s cognitive abilities to process and analyze information gradually deteriorate, resulting in mental decline. In recent years, there has been a notable increase in endeavors aimed at identifying Alzheimer’s disease and addressing its progression. Research studies have demonstrated the significant involvement of genetic factors, stress, and nutrition in developing this condition. The utilization of computer-aided analysis models based on machine learning and artificial intelligence has the potential to significantly enhance the exploration of various neuroimaging methods and non-image biomarkers. This study conducts a comparative assessment of more than 80 publications that have been published since 2017. Alzheimer’s disease detection is facilitated by utilizing fundamental machine learning architectures such as support vector machines, decision trees, and ensemble models. Furthermore, around 50 papers that utilized a specific architectural or design approach concerning Alzheimer’s disease were examined. The body of literature under consideration has been categorized and elucidated through the utilization of data-related, methodology-related, and medical-fostering components to illustrate the underlying challenges. The conclusion section of our study encompasses a discussion of prospective avenues for further investigation and furnishes recommendations for future research activities on the diagnosis of Alzheimer’s disease.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13148298