Machine Learning for Detection of Cognitive Impairment
The detection of cognitive problems, especially in the early stages, is critical and the method by which it is diagnosed is manual and depends on one or more specialist doctors, to diagnose it as the cognitive decline escalates into the early stage of dementia, e.g., Alzheimer's disease (AD). T...
| Authors: | , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2022 |
| Country: | Argentina |
| Institution: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repository: | CONICET Digital (CONICET) |
| Language: | English |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/214993 |
| Online Access: | http://hdl.handle.net/11336/214993 |
| Access Level: | Open access |
| Keyword: | MACHINE LEARNING ALZHEIMER DISEASE IMPAIRMENT MILD COGNITIVE https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
| Summary: | The detection of cognitive problems, especially in the early stages, is critical and the method by which it is diagnosed is manual and depends on one or more specialist doctors, to diagnose it as the cognitive decline escalates into the early stage of dementia, e.g., Alzheimer's disease (AD). The early stages of AD are very similar to Mild Cognitive Impairment (MCI); it is essential to identify the possible factors associated with the disease. This research aims to demonstrate that automated models can differentiate and classify MCI and AD in the early stages. The present research used a combination of Machine Learning (ML) algorithms to identify AD, using gene expressions. The algorithms used for the classification of cognitive problems and healthy people (control) were: Linear Regression, Decision Trees (DT), Naîve Bayes (NB) and Deep Learning (DP). The result of this research shows ML algorithms can identify AD, in early stages, with an 80% accuracy, using a Deep Learning (DL) algorithm. |
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