Using Machine Learning for Recognition of Alzheimer’s Disease Based on Transcription Information
https://doi.org/10.35596/1729-7648-2023-21-6-106-112
Abstract
The purpose of this article is to perform analytical and prognostic studies on the recognition of Alzhei mer’s disease based on decoded text speech data using machine learning algorithms. The data used in this article is taken from the ADReSS 2020 Challenge program, which contains speech data from patients with Alzhei mer’s disease and healthy people. The problem under study is a binary classification problem. First, the full texts of the interviewees were extracted from the transcribed texts of the speech data. This was followed by training the model based on vectorized text features using a random forest classifier, in which the authors used the GridSearchCV method to optimize hyperparameters. The classification accuracy of the model reached 85.2 %.
About the Authors
U. A. Vishniakou
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Dr. of Sci. (Tech.), Professor at the Department of Infocommunication Technologies
220013, Minsk, P. Brovki St., 6
Tel.: +375 44 486-71-82
Chu Yue Yu
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Postgraduate at the Department of Infocommunication Technologies
Minsk
References
1. Martínez-Sánchez F., Meilán J. J. G., Carro J., Ivanova O. (2018) A Prototype for the Voice Analysis Diagnosis of Alzheimer’s Disease. Journal of Alzheimer’s Disease. 64 (2), 473–481.
2. Pulido M. L. B., Hernández J. B. A., Ballester M. Á. F., González C. M. T., Mekyska J., Smékal Z. (2020) Alzheimer’s Disease and Automatic Speech Analysis: A Review. Expert Systems with Applications. 150, 113213.
3. Lorenz K., Freddolino P. P., Comas-Herrera A., Knapp M., Damant J. (2019) Technology-Based Tools and Services for Peop le with Dementia and Carers: Mapping Technology Onto the Dementia Care Pathway. Dementia. 18 (2), 725–741.
4. Szatloczki G., Hoffmann I., Vincze V., Kalman J., Pakaski M. (2015) Speaking in Alzheimer’s Disease, is That an Early Sign? Importance of Changes in Language Abilities in Alzheimer’s Disease. Frontiers in Aging Neuroscience. (7), 195.
5. Fraser K. C., Meltzer J. A., Rudzicz F. (2016) Linguistic Features Identify Alzheimer’s Disease in Narrative Speech. Journal of Alzheimer’s Disease. 49 (2), 407–422.
6. Gosztolya G., Vincze V., Tóth L. Pákáski M., Kálmán János, Hoffmann Ildikó (2019) Identifying Mild Cognitive Impairment and Mild Alzheimer’s Disease Based on Spontaneous Speech Using ASR and Linguistic Features. Computer Speech & Language. 53, 181–197.
7. Garreta R., Moncecchi G. (2013) Learning Scikit-Learn: Machine Learning in Python. Great Britain, Packt Publ.
8. Luz S., Haider F., de la Fuente S., Fromm D., MacWhinney B. (2004) Alzheimer’s Dementia Recognition Through Spontaneous Speech: The ADReSS Challenge. arXiv Preprint arXiv. 06833, 2172–2176.
9. Pompili A., Abad A., de Matos D. M. (2020) Pragmatic Aspects of Discourse Production for the Automatic Identification of Alzheimer’s Disease. IEEE Journal of Selected Topics in Signal Processing. 14 (2), 261–271.
10. Brian MacWhinney (2014) The CHILDES Project: Tools for Analyzing Talk. Vol. II: The Database. Moscow, Psychology Publ. 432.
11. Manning C. D. (2009) An Introduction to Information Retrieval. Cambridge, Cambridge University Publ. 581.
12. Breiman L. (2001) Random Forests. Machine Learning. 45, 5–32.
13. Yuan J., Bian J., Cai X., Huang J, Ye Z., Church K. (2020) Disfluencies and Fine-Tuning Pre-Trained Language Models for Detection of Alzheimer’s Disease. Interspeech. 2020, 2162–2166.
Review
For citations:
Vishniakou U.A., Yu Ch. Using Machine Learning for Recognition of Alzheimer’s Disease Based on Transcription Information. Doklady BGUIR. 2023;21(6):106-112. https://doi.org/10.35596/1729-7648-2023-21-6-106-112
Views:
360
Email this article 