Development of Intelligent Parkinson Disease Detection System Based on Machine Learning Techniques Using Speech Signal

Mohammed Younis Thanoun, Mohammad Tariq Yaseen, A.M. Aleesa


Parkinson's disease is a brain condition that induces difficulty walking, standing, concentrating, trembling, and weakness. Parkinson's symptoms typically begin slowly and increase with time. Whenever the condition develops, individuals can experience trouble walking and communicating to others. Old people mostly tend to suffer from this disease and the number is expected to increase in the future. Machine learning (ML) techniques could help in the medical field in processing and analyzing data that offer good solutions in this field in terms of high accuracy and less required time compared to conventional methods. In this study, we proposed an enhanced methodology based on utilizing SMOTE to balance the dataset, due to the available dataset is imbalanced. then adopted extra tree classifier with k-fold technique after we balanced the dataset with SMOTE. we have achieved the best accuracy with respect to the classification accuracy in the literature, the obtained accuracy of our proposed model was higher than the used approaches in the related works. The new model for classifying the Parkinson's disease-dataset with class-imbalance data distribution achieved an accuracy of 96.52% by using our proposed method. The result shown that the dataset is lacked of balancing and it proves that the balancing in the dataset is important specially in medical classification. The impact of Optimal function selection, either automated by PCA or manually carried out, is clearly still being studied, and plays an essential role in improving the performance of machine learning.


Parkinson's disease; machine learning; PD, SMOTE; extra tree classifier.

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J. M. Bassetto, B. S. Zeigelboim, A. L. Jurkiewicz, and K. F. Klagenberg, "Neurotological findings in patients with Parkinson's disease," Brazilian journal of otorhinolaryngology, vol. 74, no. 3, pp. 350-355, 2008.

B. Pillon, B. Dubois, G. Cusimano, A.-M. Bonnet, F. Lhermitte, and Y. Agid, "Does cognitive impairment in Parkinson's disease result from non-dopaminergic lesions?," Journal of Neurology, Neurosurgery & Psychiatry, vol. 52, no. 2, pp. 201-206, 1989.

B. R. Bloem and M. Munneke, "Revolutionising management of chronic disease: the ParkinsonNet approach," Bmj, vol. 348, p. g1838, 2014.

H.-L. Chen, G. Wang, C. Ma, Z.-N. Cai, W.-B. Liu, and S.-J. Wang, "An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson׳ s disease," Neurocomputing, vol. 184, pp. 131-144, 2016.

D. Calne, B. Snow, and C. Lee, "Criteria for diagnosing Parkinson's disease," Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, vol. 32, no. S1, pp. S125-S127, 1992.

H.-L. Chen et al., "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.

C. Sommer and D. W. Gerlich, "Machine learning in cell biology–teaching computers to recognize phenotypes," J Cell Sci, vol. 126, no. 24, pp. 5529-5539, 2013.

M. Nilashi, O. Bin Ibrahim, A. Mardani, A. Ahani, and A. Jusoh, "A soft computing approach for diabetes disease classification," Health Informatics Journal, vol. 24, no. 4, pp. 379-393, 2018.

S. Ozer et al., "Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI," Medical physics, vol. 37, no. 4, pp. 1873-1883, 2010.

B. Fritzke, "Growing cell structures—a self-organizing network for unsupervised and supervised learning," Neural networks, vol. 7, no. 9, pp. 1441-1460, 1994.

Ö. Eskidere, F. Ertaş, and C. Hanilçi, "A comparison of regression methods for remote tracking of Parkinson’s disease progression," Expert Systems with Applications, vol. 39, no. 5, pp. 5523-5528, 2012.

M. Hariharan, K. Polat, and R. Sindhu, "A new hybrid intelligent system for accurate detection of Parkinson's disease," Computer methods and programs in biomedicine, vol. 113, no. 3, pp. 904-913, 2014.

T. Peterek, P. Dohnálek, P. Gajdoš, and M. Šmondrk, "Performance evaluation of Random Forest regression model in tracking Parkinson's disease progress," in 13th International Conference on Hybrid Intelligent Systems (HIS 2013), 2013: IEEE, pp. 83-87.

D.-C. Li, C.-W. Liu, and S. C. Hu, "A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets," Artificial Intelligence in Medicine, vol. 52, no. 1, pp. 45-52, 2011.

G. S. Babu and S. Suresh, "Parkinson’s disease prediction using gene expression–A projection based learning meta-cognitive neural classifier approach," Expert Systems with Applications, vol. 40, no. 5, pp. 1519-1529, 2013.

P.-F. Guo, P. Bhattacharya, and N. Kharma, "Advances in detecting Parkinson’s disease," in International Conference on Medical Biometrics, 2010: Springer, pp. 306-314.

F. Åström and R. Koker, "A parallel neural network approach to prediction of Parkinson’s Disease," Expert systems with applications, vol. 38, no. 10, pp. 12470-12474, 2011.

M. Nilashi, O. Ibrahim, H. Ahmadi, L. Shahmoradi, and M. Farahmand, "A hybrid intelligent system for the prediction of Parkinson's Disease progression using machine learning techniques," Biocybernetics and Biomedical Engineering, vol. 38, no. 1, pp. 1-15, 2018.

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.

W. Froelich, K. Wrobel, and P. Porwik, "Diagnosis of Parkinson's disease using speech samples and threshold-based classification," Journal of Medical Imaging and Health Informatics, vol. 5, no. 6, pp. 1358-1363, 2015.

D. Avci and A. Dogantekin, "An expert diagnosis system for parkinson disease based on genetic algorithm-wavelet kernel-extreme learning machine," Parkinson’s disease, vol. 2016, 2016.

A. Ozcift, "SVM feature selection based rotation forest ensemble classifiers to improve computer-aided diagnosis of Parkinson disease," Journal of medical systems, vol. 36, no. 4, pp. 2141-2147, 2012.

W.-L. Zuo, Z.-Y. Wang, T. Liu, and H.-L. Chen, "Effective detection of Parkinson's disease using an adaptive fuzzy k-nearest neighbor approach," Biomedical Signal Processing and Control, vol. 8, no. 4, pp. 364-373, 2013.

J. A. Logemann, H. B. Fisher, B. Boshes, and E. R. Blonsky, "Frequency and cooccurrence of vocal tract dysfunctions in the speech of a large sample of Parkinson patients," Journal of Speech and hearing Disorders, vol. 43, no. 1, pp. 47-57, 1978.

L. Parisi, N. RaviChandran, and M. L. Manaog, "Feature-driven machine learning to improve early diagnosis of Parkinson's disease," Expert Systems with Applications, vol. 110, pp. 182-190, 2018.

M. S. Islam, I. Parvez, H. Deng, and P. Goswami, "Performance comparison of heterogeneous classifiers for detection of Parkinson's disease using voice disorder (dysphonia)," in 2014 International Conference on Informatics, Electronics & Vision (ICIEV), 2014: IEEE, pp. 1-7.

V. Despotovic, T. Skovranek, and C. Schommer, "Speech Based Estimation of Parkinson’s Disease Using Gaussian Processes and Automatic Relevance Determination," Neurocomputing, 2020.

K. Polat, "A Hybrid Approach to Parkinson Disease Classification Using Speech Signal: The Combination of SMOTE and Random Forests," in 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), 2019: IEEE, pp. 1-3.

C. O. Sakar et al., "A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform," Applied Soft Computing, vol. 74, pp. 255-263, 2019.

[Online]. Available:, (last accessed: April, 2020). .

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," Journal of artificial intelligence research, vol. 16, pp. 321-357, 2002.

Y. Xie, Y. Liu, and Q. Fu, "Imbalanced data sets classification based on SVM for sand-dust storm warning," Discrete Dynamics in Nature and Society, vol. 2015, 2015.

P. Geurts, D. Ernst, and L. Wehenkel, "Extremely randomized trees," Machine learning, vol. 63, no. 1, pp. 3-42, 2006.

H. Gunduz, "Deep Learning-Based Parkinson’s Disease Classification Using Vocal Feature Sets," IEEE Access, vol. 7, pp. 115540-115551, 2019.

I. Nissar, D. Rizvi, S. Masood, and A. Mir, "Voice-Based Detection of Parkinson’s Disease through Ensemble Machine Learning Approach: A Performance Study," EAI Endorsed Transactions on Pervasive Health and Technology, vol. 5, no. 19, 2019.

G. Solana-Lavalle, J.-C. Galán-Hernández, and R. Rosas-Romero, "Automatic Parkinson disease detection at early stages as a pre-diagnosis tool by using classifiers and a small set of vocal features," Biocybernetics and Biomedical Engineering, vol. 40, no. 1, pp. 505-516, 2020.



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