Authors:
S. Jeyantha Jafna Juliet,D. Jasmine David,J. S. Raj Kumar,Angelin Jeba P.,R. Golden Nancy,M. Selvarathi,T. Jemima Jebaseeli,DOI NO:
https://doi.org/10.26782/jmcms.2025.04.00009Keywords:
KNN,logistic regression,machine learning,Naive Bayes,Parkinson’s Disease,Speech Disorder,Abstract
Motor symptoms, such as tremors, bradykinesia, stiffness, and posture issues, are produced by the loss of dopamine-producing neurons in the spinal column portion of the brain, which is characteristic of Parkinson's disease (PD). To properly control and treat PD, the condition must be identified as soon as possible. Machine learning techniques, which use data-driven methodologies, provide intriguing possibilities for reaching this aim. These methods involve the analysis of various types of data, including clinical assessments, imaging scans, and genetic markers, to develop accurate predictive models. Even in the initial stages of the conditions, machine learning techniques can discriminate between patients who have and do not have PD by identifying minor variations and traits from such multivariate data. These models support early diagnosis and enable personalized treatment strategies tailored to the specific needs of patients. Additionally, integrating wearable sensors and mobile health technologies further enhances the feasibility of continuous monitoring and early detection, providing patients and healthcare practitioners with the tools they need to manage PD proactively. To identify diseases, one can access vast databases of medical information. To diagnose PD, the proposed method uses two different data sets. Algorithms for machine learning are also capable of helping in producing specific details from such data. The proposed research applies a few Machine Learning ways to anticipate Parkinson's disease by human guidance, with the dataset acting as the source of the process understanding. By applying the hyperparameter optimization process, the accuracy is estimated. When used to diagnose Parkinson's disease (PD), the proposed methods produce accuracy rates of 98.9% for Naive Bayes and 97.3% for Logistic Regression.Refference:
I. Anastasia M Bougea, Nikolas Papagiannakis, Athina-Maria Simitsi, & Leonidas Stefanis. (2023). Ambiental Factors in Parkinson’s disease Progression: A Systematic Review. Medicina (Kaunas, Lithuania), 59(2). 10.3390/medicina59020294.
II. Arora, S., & Paliwal, K. K. (2020). Early diagnosis of Parkinson’s disease using deep learning and machine learning techniques: A review. Journal of Neural Engineering, 17(3), 031001. 10.1109/ACCESS.2020.3016062
III. Arora, S., & Paliwal, K. K. (2021). An ensemble classifier approach for early detection of Parkinson’s disease using handwriting dynamics. Biocybernetics and Biomedical Engineering, 41(3), 1175-1187. 10.1016/j.compbiomed.2023.107031
IV. Delrobaei, M., Baktash, N., & Gilmore, G. (2018). Wearable sensor-based classification of Parkinson’s disease tremor and essential tremor using hybrid dual tree complex wavelet transform-based features. Journal of Neuro Engineering and Rehabilitation, 15(1), 95. 10.1109/TNSRE.2017.2690578
V. Gao, F., Zhang, J., & Duan, H. (2020). Diagnosis of Parkinson’s Disease Using a Stacked Deep Polynomial Network Based on Functional Magnetic Resonance Imaging Data. Frontiers in Neuroscience, 14, 586. 10.1186/s40035-015-0039-8
VI. Giuffrida, J. P., Riley, D. E., & Maddux, B. N. (2020). Wearable sensors for advanced therapy referral in Parkinson’s disease. Journal of Parkinson’s Disease, 10(1), 373-379. doi: 10.1002/mds.22445
VII. Guo, Y., Huang, D., Zhang, W., Wang, L., Li, Y., Olmo, G., … & Chan, P. (2022). High-accuracy wearable detection of freezing of gait in Parkinson’s disease based on pseudo-multimodal features. Computers in Biology and Medicine, 146, 105629. 10.1016/j.compbiomed.2022.105629
VIII. Jankovic, J. (2020). Parkinson’s disease: clinical features and diagnosis. Journal of Neurology. Neurosurgery & Psychiatry, 91(8), 795-808. 10.1136/jnnp.2007.131045
IX. Liu, J., Du, H., Bi, Q., Liao, H., & Pan, Y. (2022, December). MEST: Multi-plane Embedding and Spatial-temporal Transformer for Parkinson’s disease diagnosis. In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 1072-1077. 10.1109/BIBM55620.2022.9995498.
X. Moshkova, A., Samorodov, A., Voinova, N., Volkov, A., Ivanova, E., & Fedotova, E. (2020). Parkinson’s disease detection by using machine learning algorithms and hand movement signal from Leap Motion sensor, In 2020 26th Conference of Open Innovations Association, 321-327. 10.23919/FRUCT48808.2020.9087433.
XI. Pahuja, G., & Nagabhushan, T. N. (2021). A comparative study of existing machine learning approaches for Parkinson’s disease detection. IETE Journal of Research, 67(1), 4-14. 10.1080/03772063.2018.1531730
XII. Ponsen, M.M., Stoffers, D., Booij, J., van Eck‐Smit, B.L., Wolters, E.C., & Berendse, H.W. (2004). Idiopathic hyposmia as a preclinical sign of Parkinson’s disease. Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, 56(2),173-181. 10.1:002/ana.20160
XIII. Prashanth, R., Roy, S.D., Mandal, P.K., & Ghosh, S. (2016). High-accuracy detection of early Parkinson’s disease through multimodal features and machine learning. International Journal of Medical Informatics, 90, 13-21. 10.1016/j.ijmedinf.2016.03.001
XIV. Salari, N., Kazeminia, M., Sagha, H., Daneshkhah, A., Ahmadi, A., & Mohammadi, M. (2023). The performance of various machine learning methods for Parkinson’s disease recognition: a systematic review. Current Psychology, 42(20), 16637-16660. 10.1007/s12144-022-02949-8
XV. Sharma, S., Jain, A., & Nowacki, A. S. (2021). Deep learning-based diagnosis of Parkinson’s disease using statistical features and convolutional neural networks. Computers in Biology and Medicine, 130, 104187. doi:10.48550/arXiv.2101.05631
XVI. Silveira‐Moriyama, L., Carvalho, M.D.J., Katzenschlager, R., Petrie, A., Ranvaud, R., Barbosa, E.R., & Lees, A.J. (2008). The use of smell identification tests in the diagnosis of Parkinson’s disease in Brazil, Movement disorders: official journal of the Movement Disorder Society, 23(16), 2328-2334. 10.1002/mds.22241
XVII. Skibinska, J., & Burget, R. (2020). Parkinson’s disease detection based on changes of emotions during speech. In 2020 12th International Congress on Ultra-Modern Telecommunications and Control Systems and Workshops, 124-130. 10.1109/ICUMT51630.2020.9222446.
XVIII. Vikas Ukani (2020) Parkinson’s Disease Data Set, https://www.kaggle.com/datasets/vikasukani/parkinsons-disease-data-set
XIX. Mhetre, H., Kaduskar, V., Chougule, P., Chendake, Y., Naik, N., Hiremath, P., & Bhat, R. (2024). Binder Molecular Weight, Concentration, and Flow Rate Optimization for ZnO Nanofiber Synthesis for Electronic Device Applications. Engineering Proceedings, 59(1), 177.
XX. Jomy, J., Sharma, S., Prabhu, P. R., Hiremath, P., & Prabhu, D. (2023). Microstructural changes and their influence on corrosion post-annealing treatment of copper and AISI 5140 steel in 3.5 wt% NaCl medium. Cogent Engineering, 10(1), 2244770.
XXI. Shivaprakash, Y. M., Prabhu, S., Anne, G., Gurumurthy, B. M., Hiremath, P., Sharma, S., & Sowrabh, B. S. (2023). High-temperature dry sliding wear behaviour of pre-aged 3-step T6-treated Al7075 hybrid matrix composite. Cogent Engineering, 10(1), 2235820.
XXII. Sharma Uppangala, R., Pai, S., Patil, V., Smriti, K., Naik, N., Shetty, R., … & Rathnakar, R. (2022). Influence of thermal and thermomechanical stimuli on dental restoration geometry and material properties of cervical restoration: a 3D finite element analysis. Journal of Composites Science, 7(1), 6.
XXIII. Prabhu, D., Hiremath, P., Prabhu, P. R., & Gowrishankar, M. C. (2022). Optimization of the parameters influencing the control of dual-phase AISI1040 steel corrosion in sulphuric acid solution with pectin as inhibitor using response surface methodology. Protection of Metals and Physical Chemistry of Surfaces, 58(2), 394-413.
XXIV. Vu, T.C., Nutt, J.G., & Holford, N.H. (2012). Progression of motor and nonmotor features of Parkinson’s disease and their response to treatment. British journal of clinical pharmacology, 74(2), 267-283. 10.1111/j.1365-2125.2012.04192.x
XXV. Wroge, T.J., Ozkanca, Y., Demiroglu, C., Si, D., Atkins, D.C., & Ghomi, R.H. (2018). Parkinson’s disease diagnosis using machine learning and voice. In 2018 IEEE signal processing in medicine and biology symposium, 1-7. 10.1109/SPMB.2018.8615607