Machine Learning Approaches for Speech-Based Alzheimer’s Detection: A Comprehensive Survey
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that significantly impairs cognitive functions, leading to memory loss and other behavioral changes. It is the seventh leading cause of death worldwide, with millions of people affected. Early and accurate detection of AD is critic...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/14/2/36 |
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| Summary: | Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that significantly impairs cognitive functions, leading to memory loss and other behavioral changes. It is the seventh leading cause of death worldwide, with millions of people affected. Early and accurate detection of AD is critical for improving patient outcomes and slowing disease progression. Recent advancements in machine learning (ML) and deep learning (DL) models have demonstrated significant potential for detecting AD using patient’s speech signals, as subtle changes in speech patterns, such as reduced fluency, pronunciation difficulties, and cognitive decline, can serve as early indicators of the disease, offering a non-invasive and cost-effective method for early diagnosis. This survey paper provides a comprehensive review of the current literature on the application of ML and DL techniques for AD detection through the analysis of a patient’s speech signal, utilizing various acoustic and textual features. Moreover, it offers an overview of the changes in the brain caused by the disease, associated risk factors, publicly available datasets, and future directions for leveraging ML and DL in the detection of AD. |
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| ISSN: | 2073-431X |