Parkinson’s Insight: Leveraging CNN and LSTM Networks for Enhanced Diagnostic Accuracy

DOI

10.2991/978-94-6463-700-7_14How to use a DOI?

Keywords

Parkinson’s Disease; long short-term memory; convolution neural network; hybrid approach; medical diagnostics; Deep learning; Feature extraction

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that disturbs millions worldwide and is characterized by symptoms such as tremors, rigidity, and impaired motor function. Early detection is crucial for timely intervention, yet conventional investigative approaches often lack the sensitivity to identify PD in its early stages. This study introduces a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for detecting PD using biomedical voice recordings. The model harnesses CNN’s capability for feature extraction and LSTM’s strength in processing sequential data, enhancing detection accuracy. Three datasets—the UCI ML Repository, Oxford Parkinson’s Disease Detection, and Kaggle Parkinson’s Telemonitoring—were utilized for training and evaluation. Experimental results reveal that the hybrid CNN-LSTM model surpasses the presentation of standalone CNN and LSTM models in terms of accuracy, precision, recall, and F1 scores, achieving accuracies of 93%, 95%, and 94%, respectively. These outcomes highlight the model’s possible role as a non-invasive, accurate, and early detection tool for Parkinson’s disease, offering a promising pathway for improved patient outcomes through timely interventions.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Nirav Patel
AU  - R. Srividhya
AU  - P. Edith Linda
AU  - Sudha Rajesh
AU  - Vaibhav C. Gandhi
AU  - Vimal Bhatt
PY  - 2025
DA  - 2025/04/19
TI  - Parkinson’s Insight: Leveraging CNN and LSTM Networks for Enhanced Diagnostic Accuracy
BT  - Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
PB  - Atlantis Press
SP  - 157
EP  - 173
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-700-7_14
DO  - 10.2991/978-94-6463-700-7_14
ID  - Patel2025
ER  -