Abstract

Memory and other cognitive parts are significantly affected by Alzheimer's disease (AD). As there is no fix, early disease movement location and deferral are basic for overseeing Promotion. Verbal familiarity is one of the most widely recognized and delicate neuropsychological techniques utilized for identifying and assessing mental decreases in Promotion, in which a subject should name many things as conceivable in 30 or 60 seconds that have a place within a certain classification. A verb fluency (VF) task, a specific subset of verbal fluency that examines the subjects' list of verbs during a given period, is used in this study to develop a method for detecting AD. To identify the possibility of AD, we make use of natural language processing (NLP), random forest (RF), neural network (NN), and recurrent neural network (RNN) machine learning strategies. However, preprocessing the data is required for the developed models to stratify subjects into the appropriate AD and control groups with up to 76% accuracy using RF. This exactness is marginally lower, however not fundamentally, at 67% utilizing RNN and NLP, which includes practically no manual information preprocessing. Utilizing specific VF tasks for early AD detection is now possible, thanks to this study's results.

References

1. A. Association, “Dementia vs. alzheimer’s disease: What is the difference?” 2021. [Online]. Available: https://www.alz.org/alzheimersdementia/ difference-between-dementia-and-alzheimer-s
2. A. N. Today, “Alzheimer’s disease statistics.” [Online]. Available: https://alzheimersnewstoday.com/alzheimers-disease-statistics
3. J. Elflein, “Death rate due to alzheimer’s disease in the u.s. 2000- 2019,” Alzheimer’s Association, 2021.
4. W. Jarrold, B. Peintner, D. Wilkins, D. Vergryi, C. Richey, M. L. Gorno-Tempini, and J. Ogar, “Aided diagnosis of dementia type through computer-based analysis of spontaneous speech,” in Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. Baltimore, Maryland, USA: Association for Computational Linguistics, Jun. 2014, pp. 27–37. [Online]. Available: https://www.aclweb.org/anthology/W14-3204
5. D. Shibata, S. Wakamiya, A. Kinoshita, and E. Aramaki, “Detecting Japanese patients with Alzheimer’s disease based on word category frequencies,” in Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP). Osaka, Japan: The COLING 2016 Organizing Committee, Dec. 2016, pp. 78–85. [Online]. Available: https://www.aclweb.org/anthology/W16-4211
6. K. Palmer, L. B¨ackman, B. Winblad, and L. Fratiglioni, “Detection of alzheimer’s disease and dementia in the preclinical phase: population based cohort study,” Bmj, vol. 326, no. 7383, p. 245, 2003.
7. E. J. Paek and L. L. Murray, “Quantitative and qualitative analysis of verb fluency performance in individuals with probable alzheimer’s disease and healthy older adults,” American journal of speech-language pathology, vol. 30, no. 1S, pp. 481–490, 2021.
8. Q.-Y. Zhong, E. W. Karlson, B. Gelaye, S. Finan, P. Avillach, J. W. Smoller, T. Cai, and M. A. Williams, “Screening pregnant women for suicidal behavior in electronic medical records: diagnostic codes vs. clinical notes processed by natural language processing,” BMC medical informatics and decision making, vol. 18, no. 1, pp. 1–11, 2018.
9. V. K¨epuska and G. Bohouta, “Next-generation of virtual personal assistants (microsoft cortana, apple siri, amazon alexa and google home),” in 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), 2018, pp. 99–103.
10. T. Gadekallu, A. Soni, D. Sarkar, and L. Kuruva, “Application of sentiment analysis in movie reviews,” in Sentiment Analysis and Knowledge Discovery in Contemporary Business. IGI Global, 2019, pp. 77–90.
11. M. Nguyen, T. He, L. An, D. C. Alexander, J. Feng, B. T. Yeo, A. D. N. Initiative et al., “Predicting alzheimer’s disease progression using deep recurrent neural networks,” NeuroImage, vol. 222, p. 117203, 2020.
12. H. Li and Y. Fan, “Early prediction of alzheimer’s disease dementia based on baseline hippocampal mri and 1-year follow-up cognitive measures using deep recurrent neural networks,” in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE, 2019, pp. 368–371.
13. D. A. Balota, M. J. Yap, K. A. Hutchison, M. J. Cortese, B. Kessler, B. Loftis, J. H. Neely, D. L. Nelson, G. B. Simpson, and R. Treiman, “The english lexicon project,” Behavior research methods, vol. 39, no. 3, pp. 445–459, 2007.
14. L. Breiman, “Random forests,” Machine learning, vol. 45, no. 1, pp. 5–32, 2001.
15. B. M¨uller, Neural Networks An Introduction, ser. Physics of Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995.
16. F. Chollet et al., “Keras,” https://keras.io, 2015.
17. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Man´e, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Vi´egas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: https://www.tensorflow.org/
18. M. Honnibal and I. Montani, “spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing,” 2017, to appear.
19. P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to data mining. Pearson Education India, 2016.
20. M. Hossin and M. Sulaiman, “A review on evaluation metrics for data classification evaluations,” International Journal of Data Mining & Knowledge Management Process, vol. 5, no. 2, p. 1, 2015.
21. J. E. Gaugler, R. L. Kane, J. A. Johnston, and K. Sarsour, “Sensitivity and specificity of diagnostic accuracy in alzheimer’s disease: a synthesis of existing evidence,” American Journal of Alzheimer’s Disease & Other Dementias®, vol. 28, no. 4, pp. 337–347, 2013.

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