Early detection of Alzheimer’s disease using machine learning algorithms

Alzheimer’s disease (AD) is a neurodegenerative disorder with a growing global impact, making early diagnosis and prediction critical for effective treatment. It affects the memory and cognitive functions of a patient beyond that expected of biological aging. The brain cells progressively shrink and...

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Hauptverfasser: Mrunal, Rane, Samantaray, Sarada
Format: Tagungsbericht
Sprache:eng
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Zusammenfassung:Alzheimer’s disease (AD) is a neurodegenerative disorder with a growing global impact, making early diagnosis and prediction critical for effective treatment. It affects the memory and cognitive functions of a patient beyond that expected of biological aging. The brain cells progressively shrink and die with irreversible cell damage. The significance of early detection of AD is to manage the symptoms to delay this irreversible brain damage and extend the patient’s quality of life. According to the World Health Organization, there are 55 million existing dementia cases worldwide with an increment of about 10 million cases every year. This work presents a comprehensive overview of the use of Machine Learning (ML) techniques for the prediction of AD. The project aims at early diagnosis of AD using the information from MRI images, biomarkers specific to AD, and cognitive screening tests. The data used for this project is acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). In this study, the simple ML models are developed based on the longitudinal volumetric data of different brain regions such as the Hippocampus, Ventricles, Entorhinal cortex, Fusiform, Middle Temporal Cortex region as well as whole brain, and intracranial volume (ICV). The data consists of the participants who are classified as cognitively normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), Alzheimer’s Disease (AD), and Significant Memory Concern (SMC). The difference in the volume of certain brain regions of CN people and other types of participants was statistically different (t-test, p
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0234297