Abstract
Alzheimer’s disease (AD) is the most leading symptom of neurodegenerative dementia; AD is defined now as one of the most costly chronic diseases. For that automatic diagnosis and control of Alzheimer’s disease may have a significant effect on society along with patient well-being. The Mini Mental State Examination (MMSE) is a prominent method for identifying whether a person might have dementia and about the dementia severity respectively. These methods are time-consuming and require well-educated personnel to administer.
This study investigates another method for predicting MMSE score based on the language deterioration of people, using linguistic information from speech samples of picture description task.
We use a regression model over a set of 169 patients with different degrees of dementia; we achieve a Mean Absolute Error (MAE) of 3.6 for MMSE. When focusing on selecting the best features, we improve the MAE to 0.55. Obtained results indicate that the proposed taxonomy of the linguistic features could operate as a cheap dementia test, probably also in non-clinical situations.
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Ben Ammar, R., Ben Ayed, Y. (2021). A Language-Based Approach for Predicting Alzheimer Disease Severity. In: Cherfi, S., Perini, A., Nurcan, S. (eds) Research Challenges in Information Science. RCIS 2021. Lecture Notes in Business Information Processing, vol 415. Springer, Cham. https://doi.org/10.1007/978-3-030-75018-3_19
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DOI: https://doi.org/10.1007/978-3-030-75018-3_19
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