Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{174648,
        author = {Kowseelan S and Mohana Sundaram R and Lishvanth Nanda B and Terris Raja S},
        title = {Parkinson’s Disease Prediction Using Deep Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {116-121},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174648},
        abstract = {In recent years, significant progress has been made in the field of medical diagnosis, but early and accurate detection of Parkinson's disease (PD) remains a challenge. Current technology cannot accurately detect this neurodegenerative condition, emphasizing the need for current research. Parkinson's disease is a complex neurological disease whose prevalence is increasing worldwide. Timely and accurate diagnosis is crucial for optimal patient care and treatment. This study addresses the critical issue of improving PD diagnosis by using convolutional neural networks (CNN) to analyze spiral and wave graphs. The methodology involves the development of a robust CNN model trained on a diverse dataset containing drawings of PD and healthy individuals. This dataset is the basis for training and testing the model and ensures its ability to distinguish PD cases from non-PD cases. The results show the exceptional accuracy of the CNN model in predicting PD of spiral and wave patterns with a classification accuracy of more than 90%. In addition, this study highlights the promising role of deep learning methods in medical diagnosis, especially in the context of Parkinson's disease. The successful integration of a CNN model into a React application for real-time prediction and patient performance monitoring has important implications for telemedicine and remote health management.},
        keywords = {Parkinson's Disease, CNN, Spiral and Wave Drawings, Diagnosis, Deep Learning, Early Detection, Remote Monitoring.},
        month = {March},
        }