Tracking Parkinson’s Disease Progression Using Deep Learning: A Hybrid Auto Encoder and Bi-LSTM Approach

Parkinson's disease (PD) is a progressive and chronic neurodegenerative disorder characterized by motor impairment, speech deficits, and cognitive decline. Monitoring disease progression accurately and intermittently is imperative for early treatment planning and personalized intervention. In the past, conventional methods of diagnosis—clinical examination and traditional machine learning (ML) algorithms—tend to be insufficient in identifying intricate temporal behaviors of PD progress and involve frequent clinic visits. There is no cure for this disease but there are treatments. To tackle these issues, we introduce a deep learning (DL)-based approach that integrates auto encoders for feature learning with Bi-Directional Long Short-Term Memory (Bi-LSTM) networks for temporal sequence modeling. The hybrid model successfully monitors PD severity over time by learning complex patterns in the data. We measure our method with the Parkinson's Tele monitoring Dataset from the UCI Machine Learning Repository, which contains longitudinal voice recordings together with Unified Parkinson's Disease Rating Scale (UPDRS) scores—rendering it particularly well-suited for time-series analysis. Implemented in Python with Tensor Flow applies sophisticated training methods to achieve maximum performance. Experimental results affirm a dramatic improvement compared to traditional ML methods, producing an accuracy rate of 95.2%. Such high predictive power facilitates timely adjustment of treatment and improves patient management. The suggested model presents a non-invasive, scalable real-time PD monitoring solution. It aids neurologists, clinicians, and researchers by offering an AI-based platform for pre-emptive intervention. It helps patients by facilitating continuous remote monitoring, minimizing frequent clinic visits, and enhancing their quality of life.

@article{Sajja2025,
  title     = {Tracking Parkinson’s Disease Progression Using Deep Learning: A Hybrid Auto Encoder and Bi-LSTM Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Sri Lavanya Sajja and Kabilan Annadurai and S. Kirubakaran and TK Rama Krishna Rao and P. Satish and Elangovan Muniyandy and Yahia Said},
  doi       = {10.14569/IJACSA.2025.0160548},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160548}
}