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Original Article
Detection of Parkinson's Disease Using Spiral Models
Dr. D Kirubha1
Shilpa H M2
Sindhu Priya3
Swathi N4
Varsha S Poojar5
1 HOD, Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bengaluru, Karnataka, India. 2 3 4 5Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bengaluru, Karnataka, India.
Published Online: November-December 2025
Pages: 237-247
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20250506032
References
1. M. Amini, A. Fathi, M. M. S. Eslami, and H. R. Marateb, “Spiral drawing classification for Parkinson’s disease diagnosis using Inception V3 convolutional neural network,” Biomedical Signal Processing and Control, vol. 71, p. 103206, Jan. 2022.
2. W. Wang, R. Duan, J. Liu, and J. Zhang, “Parkinson’s Disease Detection From Handwriting Using Convolutional Neural Networks,” IEEE Access, vol. 9, pp. 42925–42934, 2021.
3. P. K. Maddikunta et al., “Deep learning models for Parkinson’s disease classification: A review,” Computers in Biology and Medicine, vol. 153, p.106397, Feb. 2023.
4. A. Khobragade and R. Rathod, “Parkinson’s Disease Detection Using Machine Learning Algorithms,” in Proceedings of the International Conference on Smart Electronics and Communication (ICOSEC), 2021, pp. 1464–1468.
5. Y. Park, S. Kim, and H. Lee, “Analysis of Parkinson’s Disease Diagnosis Using Deep Neural Networks on Spiral Drawing Data,” Sensors, vol. 22, no. 9, p. 3459, Apr. 2022.
6. A. Bohr and K. Memarzadeh (eds.), Artificial Intelligence in Healthcare. Academic Press, 2020. A detailed overview of AI applications in healthcare, including disease prediction and diagnostic technologies.
7. D. A. Clifton, Machine Learning for Healthcare Technologies. Institution of Engineering and Technology (IET), 2021. Covers machine learning methods for healthcare, diagnostic systems, and wearable technologies.
8. C. Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. 2nd ed., 2022. A practical resource on interpretability techniques relevant to AI in health-related fields.
9. E. Marcello and L. Pugliatti (eds.), Parkinson’s Disease: Molecular and Therapeutic Insights from Systems Biology and Artificial Intelligence. Elsevier, 2023. Explores how AI and systems biology contribute to PD research.
10. S. Consoli, D. Reforgiato Recupero, and M. Ceci, Data Science for Healthcare: Methodologies and Applications. Springer, 2021. Presents case studies involving disease prediction and explainable data-driven models.
11. IBM Research, “Explainable AI Overview.” Available:https://www.research.ibm.com/artificial intelligence/explainable-ai/ A comprehensive online resource covering XAI concepts and applications.
12. Parkinson’s Foundation, “Parkinson’s Disease Information.”Available:https://www.parkinson.org/Unders tandingParkinsons/What-is-Parkinsons Provides current medical and research information on Parkinson’s disease.
13. National Institutes of Health (NIH), “AI for Healthcare.” Available: https://www.nih.gov/research- training/medical-research-initiatives/ai-healthcare Details on NIH-supported research involving AI for healthcare.
2. W. Wang, R. Duan, J. Liu, and J. Zhang, “Parkinson’s Disease Detection From Handwriting Using Convolutional Neural Networks,” IEEE Access, vol. 9, pp. 42925–42934, 2021.
3. P. K. Maddikunta et al., “Deep learning models for Parkinson’s disease classification: A review,” Computers in Biology and Medicine, vol. 153, p.106397, Feb. 2023.
4. A. Khobragade and R. Rathod, “Parkinson’s Disease Detection Using Machine Learning Algorithms,” in Proceedings of the International Conference on Smart Electronics and Communication (ICOSEC), 2021, pp. 1464–1468.
5. Y. Park, S. Kim, and H. Lee, “Analysis of Parkinson’s Disease Diagnosis Using Deep Neural Networks on Spiral Drawing Data,” Sensors, vol. 22, no. 9, p. 3459, Apr. 2022.
6. A. Bohr and K. Memarzadeh (eds.), Artificial Intelligence in Healthcare. Academic Press, 2020. A detailed overview of AI applications in healthcare, including disease prediction and diagnostic technologies.
7. D. A. Clifton, Machine Learning for Healthcare Technologies. Institution of Engineering and Technology (IET), 2021. Covers machine learning methods for healthcare, diagnostic systems, and wearable technologies.
8. C. Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. 2nd ed., 2022. A practical resource on interpretability techniques relevant to AI in health-related fields.
9. E. Marcello and L. Pugliatti (eds.), Parkinson’s Disease: Molecular and Therapeutic Insights from Systems Biology and Artificial Intelligence. Elsevier, 2023. Explores how AI and systems biology contribute to PD research.
10. S. Consoli, D. Reforgiato Recupero, and M. Ceci, Data Science for Healthcare: Methodologies and Applications. Springer, 2021. Presents case studies involving disease prediction and explainable data-driven models.
11. IBM Research, “Explainable AI Overview.” Available:https://www.research.ibm.com/artificial intelligence/explainable-ai/ A comprehensive online resource covering XAI concepts and applications.
12. Parkinson’s Foundation, “Parkinson’s Disease Information.”Available:https://www.parkinson.org/Unders tandingParkinsons/What-is-Parkinsons Provides current medical and research information on Parkinson’s disease.
13. National Institutes of Health (NIH), “AI for Healthcare.” Available: https://www.nih.gov/research- training/medical-research-initiatives/ai-healthcare Details on NIH-supported research involving AI for healthcare.
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