Predicting mild cognitive impairment: integrating cognitive and motor features
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1258Keywords:
Prognosis, Machine Learning, Mild Cognitive ImpairmentAbstract
Objective: Mild Cognitive Impairment (MCI) represents an intermediate stage between normal aging and dementia, requiring early detection to prevent its progression. This study aims to develop a machine learning classification model to accurately predict the prognosis of individuals with MCI, differentiating them from healthy individuals. Method: The method integrates motor and cognitive variables as well as self-reported information. The SVM, KNN and XGBoost algorithms were applied. The best prediction was evaluated using the Shapley Value method to determine the importance of each variable. Results: The SVM produced the best results, achieving 88% sensitivity and revealing that the variables of the motor domain and the cognitive and motor domains are highly relevant for classification. Conclusion: In addition to being more accessible, the method developed also presented high sensitivity in MCI classification based on the integration of cognitive and motor variables.
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