Applicability of Artificial Intelligence in Smart Healthcare Systems for Automatic Detection of Parkinson’s Disease

Harikumar Pallathadka; S.J.R.K. PADMINIVALLI V.; M. Vasavi; P. Nancy; Mohd Naved; Harish Kumar; Samrat Ray

doi:10.24423/cames.2024.557

Harikumar Pallathadka Manipur International University, Ghari, India
S.J.R.K. PADMINIVALLI V. Department of Computer Science and Engineering, R.V.R. & J.C. College of Engineering, Chowdavaram, Andhra Pradesh, India
M. Vasavi Department of Computer Science and Engineering, R.V.R. & J.C. College of Engineering, Chowdavaram, Andhra Pradesh, India
P. Nancy Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India
Mohd Naved SOIL School of Business Design, Manesar, Haryana, India
Harish Kumar Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia
Samrat Ray Economics, Sunstone Eduversity, Gurgaon, India

DOI: http://dx.doi.org/10.24423/cames.2024.557

Abstract

Parkinson’s disease is associated with memory loss, anxiety, and depression in the brain. Problems such as poor balance and difficulty during walking can be observed in addition to symptoms of impaired posture and rigidity. The field dedicated to making computers capable of learning autonomously, without having to be explicitly programmed, is known as machine learning. An approach to the diagnosis of Parkinson’s disease, which is based on artificial intelligence, is discussed in this article. The input for this system is provided through photographic examples of Parkinson’s disease patient handwriting. Received photos are preprocessed using the relief feature option to begin the process. This is helpful in the process of selecting characteristics for the identification of Parkinson’s disease. After that, the linear discriminant analysis (LDA) algorithm is employed to reduce the dimensions, bringing down the total number of dimensions that are present in the input data. The photos are then classified via radial basis function-support vector machine (SVM-RBF), k-nearest neighbors (KNN), and naive Bayes algorithms, respectively.

Keywords

Parkinson’s disease detection, machine learning, relief algorithm, LDA algorithm, SVM-RBF, accuracy, sensitivity, specificity,

References

1. H. Gunduz, Deep learning-based Parkinson’s disease classification using vocal feature sets, IEEE Access, 7: 115540–115551, 2019, doi: 10.1109/ACCESS.2019.2936564.
2. B. Hurwitz, Urban observation and sentiment in James Parkinson’s “Essay on the shaking palsy” (1817), Literature and Medicine, 32(1): 74–104, 2014, doi: 10.1353/lm.2014.0002.
3. S. Grover, S. Bhartia, A. Akshama, A. Yadav, K.R. Seeja, Predicting severity of Parkinson’s disease using deep learning, Procedia Computer Science, 132: 1788–1794, 2018, doi: 10.1016/j.procs.2018.05.154.
4. P.B. Foley, D.J. Hare, K.L. Double, A brief history of brain iron accumulation in Parkinson disease and related disorders, Journal of Neural Transmission, 129: 505–520, 2022, doi: 10.1007/s00702-022-02505-5.
5. A. Liu et al., Galvanic vestibular stimulation improves subnetwork interactions in Parkinson’s disease, Journal of Healthcare Engineering, 2021: 6632394, 2021, doi: 10.1155/2021/6632394.
6. M.A. Motin, N.D. Pah, S. Raghav, D.K. Kumar, Parkinson’s disease detection using smartphone recorded phonemes in real world conditions, IEEE Access, 10: 97600–97609, 2022, doi: 10.1109/ACCESS.2022.3203973.
7. S. Dash, A systematic review of adaptive machine learning techniques for early detection of Parkinson’s disease, [in:] A. Abraham et al. [Eds.], Artificial Intelligence for Neurological Disorders, pp. 361–385, Academic Press, 2023, doi: 10.1016/B978-0-323-90277-9.00018-3.
8. V. Jasti et al., Computational technique based on machine learning and image processing for medical image analysis of breast cancer diagnosis, Security and Communication Networks, 2022: 1918379, 2022, doi: 10.1155/2022/1918379.
9. K. Kira, L.A. Rendell, Feature selection problem: traditional methods and a new algorithm, [in:] AAAI’92: Proceedings Tenth National Conference on Artificial Intelligence, pp. 129–134, 1992.
10. Z. Lu, Z. Liang, A complete subspace analysis of linear discriminant analysis and its robust implementation, Journal of Electrical and Computer Engineering, 2016: 3919472, 2016, doi: 10.1155/2016/3919472.
11. V.N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, 1995.
12. J. Platt, Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines: Advances in Kernel Methods – Support Vector Machine Learning, Cambridge, MA, USA: MIT Press, 1998.
13. C.Y. Fan, P.C. Chang, J.J. Lin, J.C. Hsieh, A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification, Applied Soft Computing, 11(1): 632–644, 2011, doi: 10.1155/2022/1918379.
14. K.V.S.R.P. Varma, A.A. Rao, T.S.M. Lakshmi, P.V.N. Rao, A computational intelligence approach for a better diagnosis of diabetic patients, Computers and Electrical Engineering, 40(5): 1758–1765, 2014, doi: 10.1016/j.compeleceng.2013.07.003.
15. P. Drotár, J. Mekyska, I. Rektorová, L. Masarová, Z. Smékal, M. Faundez-Zanuy, Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson’s disease, Artificial Intelligence in Medicine, 67: 39–46, 2016, doi: 10.1016/j.artmed.2016.01.004.
16. P. Drotár, J. Mekyska, I. Rektorová, L. Masarová, Z. Smékal, M. Faundez-Zanuy, Analysis of in-air movement in handwriting: A novel marker for Parkinson’s disease, Computer Methods and Programs in Biomedicine, 117(3): 405–411, 2014, doi: 10.1016/j.cmpb.2014.08.007.
17. P. Drotár, J. Mekyska, I. Rektorová, L. Masarová, Z. Smékal, M. Faundez-Zanuy, Decision support framework for Parkinson’s disease based on novel handwriting markers, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(3): 508–516, 2015, doi: 10.1109/TNSRE.2014.2359997.
18. M. Juutinen et al., Parkinson’s disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study, PLOS ONE, 15(7): e0236258, 2020, doi: 10.1371/journal.pone.0236258.
19. O. Asmae, R. Abdelhadi, C. Bouchaib, S. Sara, K. Tajeddine, Parkinson’s disease identification using KNN and ANN algorithms based on voice disorder, [in:] 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), pp. 1–6, 2020, doi: 10.1109/IRASET48871.2020.9092228.
20. A. Rahman, S. Rizvi, A. Khan, A.A. Abbasi, S. Khan, T. Chung, Parkinson’s disease diagnosis in cepstral domain using MFCC and dimensionality reduction with SVM classifier, Mobile Information Systems, 2021: 8822069, 2021, doi: 10.1155/2021/8822069.
21. M. Isenkul, B. Sakar, Parkinson Disease Spiral Drawings Using Digitized Graphics Tablet, UCI Machine Learning Repository, 2017, doi: 10.24432/C5Q01S.
22. B.E. Sakar et al., Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings, IEEE Journal of Biomedical and Health Informatics, 17(4): 828–834, 2013, doi: 10.1109/JBHI.2013.2245674.