Machine learning for detection of cognitive problems: a review of the literature
Abstract
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 itself. As the cognitive decline escalates into the early stage of dementia eg Alzheimer's disease (AD). The early stages of AD are very similar to Mild Cognitive Impairment, so it is essential to be able to identify the possible factors associated with the disease. The objective of this research is to demonstrate that automated models can differentiate and classify Mild Cognitive Impairment (MCI) and AD disease.
For this research, a survey methodology was used where a study of around 30 investigations is carried out where they show that Machine Learning (ML) algorithms serve as support to the specialist doctor to determine whether or not a person has Alzheimer's or some type of dementia related.
The algorithms used for the classification of cognitive problems and healthy people (control) were: decision support machines (SVM), neural networks (RN), Decision Trees (DT), and Naîve Bayes (NB).
According to the findings, the most used algorithms for classification are: SVM and Neural Networks, but the algorithm that had the best precision was NB.
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References
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