Abstract
Parkinson’s disease (PD) is one of the most rapidly growing neurodegenerative diseases in the world. Due to motor symptoms, it affects the normal life of a person. There is a severe need to identify PD in its early stage to avoid it getting worse and to control its symptoms easily. The advancements in Artificial Intelligence (AI) and the Internet of Things (IoT) open up new avenues for the analysis of various data points such as the gait of a person for early-stage detection. In this paper, we propose a methodology based on the use of Long Short-Term Memory (LSTM) architecture for PD diagnosis. We have used time series analysis to find the gait patterns and deep learning techniques to extract the features and to build a classifier model. The proposed model is predicting the PD disease with 85% testing accuracy and with an F1 score of 0.90. The validation is performed using Cohen’s Kappa statistical method and obtained a score of 0.631.
Keywords
- Parkinson’s disease
- Deep learning
- LSTM
- RNN
- Gait analysis
- IoT
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Gawade, A., Pandharkar, R., Deolekar, S., Salunkhe, U. (2021). Early Diagnosis of Parkinson’s Disease Using LSTM: A Deep Learning Approach. In: , et al. Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020). SoCPaR 2020. Advances in Intelligent Systems and Computing, vol 1383. Springer, Cham. https://doi.org/10.1007/978-3-030-73689-7_44
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DOI: https://doi.org/10.1007/978-3-030-73689-7_44
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