Advanced PCA-KNN Classification Technique for Parkinson’s disease Diagnosis at Early Stage

Despite the increasing clinical demand for accurate and objective methods to evaluate Parkinsonian tremors, machine learning–based scoring aligned with the Unified Parkinson’s Disease Rating Scale (UPDRS) is still underutilized. This study addresses this gap by employing machine learning algorithms to predict UPDRS scores in a way that mirrors the evaluation approach used by neurologists in clinical practice. Although traditional methods such as Bayesian Networks, Decision Trees, and Artificial Neural Networks have been applied to Parkinson’s Disease (PD) detection, there is room for improvement in terms of classification accuracy and model robustness. In this work, we propose an enhanced classification framework based on Principal Component Analysis (PCA) combined with the K-Nearest Neighbors (KNN) algorithm to improve the diagnostic accuracy of Parkinson’s disease. The proposed methodology is implemented in a Jupyter Notebook environment using Python, which provides a flexible and open-source platform for data preprocessing, model training, and performance evaluation. Li-braries such as Scikit-learn, NumPy, and Matplotlib are utilized for dimensionality reduction, classification, and visualization, respectively. Performance evaluation based on accuracy and precision demonstrates that our PCA-KNN model significantly outperforms conventional methods, highlighting its potential as a reliable and efficient diagnostic approach for Parkinson’s disease. Index Terms—Parkinson’s Disease (PD),Unified Parkin-son’s Disease Rating Scale (UPDRS),Machine Learning,PCA-KNN (Principal Component Analysis – K-Nearest Neigh-bors),Classification ,Dimensionality Reduction Python.

Principal Component Analysis – K-Nearest Neigh-bors

1. M. Anjali, M. M. Dominic, R. S. Rober et al., “An overview of deeplearning,” Journal of Computing and Intelligent Systems, 2017. [Google Scholar] [Crossref]

2. R. Ceylan, M. Ceylan, Y. O¨ zbay, and S. Kara, “Fuzzy clustering complex-valued neural network to diagnose cirrhosis disease,” Expert Systems with Applications, vol. 38, no. 8, pp. 9744–9751, 2011. [Google Scholar] [Crossref]

3. A. H. Al-Fatlawi, M. H. Jabardi, and S. H. Ling, “Efficient diagnosis system for parkinson’s disease using deep belief network,” in 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2016, pp. 1324–1330. [Google Scholar] [Crossref]

4. M. Peker, B. Sen, and D. Delen, “A novel method for automated diagnosis of epilepsy using complex-valued classifiers,” IEEE journal of biomedical and health informatics, vol. 20, no. 1, pp. 108–118, 2015. [Google Scholar] [Crossref]

5. H.-L. Chen, C.-C. Huang, X.-G. Yu, X. Xu, X. Sun, G. Wang, and S.-J. Wang, “An efficient diagnosis system for detection of parkinson’s disease using fuzzy k-nearest neighbor approach,” Expert systems with applications, vol. 40, no. 1, pp. 263–271, 2013. [Google Scholar] [Crossref]

6. A. Pannu, “Artificial intelligence and its application in different areas,” Artificial Intelligence, vol. 4, no. 10, pp. 79–84, 2015. [Google Scholar] [Crossref]

7. P. Guille´n-Rondo´n and M. D. Robinson, “Deep brain stimulation signal classification using deep belief networks,” in 2016 International Confer-ence on Computational Science and Computational Intelligence (CSCI). IEEE, 2016, pp. 155–158. [Google Scholar] [Crossref]

8. M. Sivachitra, R. Savitha, S. Suresh, and S. Vijayachitra, “A fully complex-valued fast learning classifier (fc-flc) for real-valued classifi-cation problems,” Neurocomputing, vol. 149, pp. 198–206, 2015. [Google Scholar] [Crossref]

9. C. Huang, W. Gong, W. Fu, and D. Feng, “A research of speech emotion recognition based on deep belief network and svm,” Mathematical Problems in Engineering, vol. 2014, 2014. [Online]. Available: https://www.hindawi.com/journals/mpe/2014/749604/ [Google Scholar] [Crossref]

10. S. Onojima, Y. Arima, and A. Hirose, “Millimeter-wave security imaging using complex-valued self-organizing map for visualization of moving targets,” Neurocomputing, vol. 134, pp. 247–253, 2014. [Google Scholar] [Crossref]

11. V. Kumar and L. Verma, “Binary classifiers for health care databases: A comparative study of data mining classification algorithms in the diagnosis of breast cancer,” International Journal of Computer Science and Technology, vol. 1, no. 2, pp. 124–129, 2010. [Google Scholar] [Crossref]

12. R. A. Shirvan and E. Tahami, “Voice analysis for detecting parkinson’s disease using genetic algorithm and knn classification method,” in 2011 18th Iranian Conference of Biomedical Engineering (ICBME). IEEE, 2011, pp. 278–283. [Google Scholar] [Crossref]

13. M. Shahbakhi, D. T. Far, and E. Tahami, “Speech analysis for diagnosis of parkinson’s disease using genetic algorithm and support vector machine,” Journal of Biomedical Science and Engineering, vol. 7, pp. 147–156, 2014. [Google Scholar] [Crossref]

14. M. D. S. T. F. on Rating Scales for Parkinson’s Disease, “The unified parkinson’s disease rating scale (updrs): status and recommendations,” Movement Disorders, vol. 18, no. 7, pp. 738–750, 2003. [Google Scholar] [Crossref]

15. T. V. Sriram, M. V. Rao, G. S. Narayana, D. Kaladhar, and T. P. R. Vital, “Intelligent parkinson disease prediction using machine learning algorithms,” International Journal of Engineering and Innovative Tech-nology (IJEIT), vol. 3, no. 3, pp. 1568–1572, 2013. [Google Scholar] [Crossref]

16. J. Brownlee, “Supervised and unsupervised machine learning algo-rithms,” Machine Learning Mastery, vol. 16, no. 03, 2016. [Google Scholar] [Crossref]

17. Y. N. Jane, H. K. Nehemiah, and K. Arputharaj, “A qbackpropagated time delay neural network for diagnosing severity of gait disturbances in parkinson’s disease,” Journal of biomedical informatics, vol. 60, pp. 169–176, 2016. [Google Scholar] [Crossref]

18. S. Sivaranjini and C. Sujatha, “Deep learning based diagnosis of parkinson’s disease using convolutional neural network,” Multimedia Tools and Applications, pp. 1–13, 2019. [Google Scholar] [Crossref]

19. C. Ma, J. Ouyang, H.-L. Chen, and X.-H. Zhao, “An efficient diagnosis system for parkinson’s disease using kernel-based extreme learning machine with subtractive clustering features weighting approach,” Com-putational and mathematical methods in medicine, vol. 2014, 2014. [Google Scholar] [Crossref]

20. H. Gu¨ru¨ler, “A novel diagnosis system for parkinson’s disease using complex-valued artificial neural network with k-means clustering feature weighting method,” Neural Computing and Applications, vol. 28, no. 7, pp. 1657–1666, 2017. [Google Scholar] [Crossref]

21. E. Benmalek, J. Elmhamdi, and A. Jilbab, “Multiclass classification of parkinson’s disease using cepstral analysis,” International Journal of Speech Technology, vol. 21, no. 1, pp. 39–49, 2018. [Google Scholar] [Crossref]

22. S. Gu¨nes¸, K. Polat, and S¸ . Yosunkaya, “Efficient sleep stage recognition system based on eeg signal using k-means clustering based feature weighting,” Expert Systems with Applications, vol. 37, no. 12, pp. 7922–7928, 2010. [Google Scholar] [Crossref]

23. H. M. Moftah, A. T. Azar, E. T. Al-Shammari, N. I. Ghali, A. E. Hassanien, and M. Shoman, “Adaptive k-means clustering algorithm for mr breast image segmentation,” Neural Computing and Applications, vol. 24, no. 7-8, pp. 1917–1928, 2014. [Google Scholar] [Crossref]

24. A. Caliskan, H. Badem, A. Basturk, and M. E. Yuksel, “Diagnosis of the parkinson disease by using deep neural network classifier,” Istanbul University-Journal of Electrical & Electronics Engineering, vol. 17, no. 2, pp. 3311–3318, 2017. [Google Scholar] [Crossref]

25. M. Leelavathi and K. S. Devi, “An architecture of deep learning method to predict traffic flow in big data,” IJRET, vol. 5, no. 4, pp. 461–468, 2016. [Google Scholar] [Crossref]

26. R. Das, “A comparison of multiple classification methods for diagnosis of parkinson disease,” Expert Systems with applications, vol. 37, no. 2, pp. 1568–1572, 2010. [Google Scholar] [Crossref]

27. K. Song, Y. Liu, R. Wang, M. Zhao, Z. Hao, and D. Qian, “Restricted boltzmann machines and deep belief networks on sunway cluster,” in 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, 2016, pp. 245–252. [Google Scholar] [Crossref]

28. A. Hirose and S. Yoshida, “Relationship between phase and amplitude generalization errors in complex-and real-valued feedforward neural networks,” Neural Computing and Applications, vol. 22, no. 7-8, pp. 1357–1366, 2013. [Google Scholar] [Crossref]

29. R. K. Sharma and A. K. Gupta, “Voice analysis for telediagnosis of parkinson disease using artificial neural networks and support vector ma-chines,” International Journal of Intelligent Systems and Applications, vol. 7, no. 6, p. 41, 2015. [Google Scholar] [Crossref]

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