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A Multi-agent Feature Selection and Hybrid Classification Model for Parkinson's Disease Diagnosis

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Published:18 May 2021Publication History
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Abstract

Parkinson's disease (PD) diagnostics includes numerous analyses related to the neurological, physical, and psychical status of the patient. Medical teams analyze multiple symptoms and patient history considering verified genetic influences. The proposed method investigates the voice symptoms of this disease. The voice files are processed, and the feature extraction is conducted. Several machine learning techniques are used to recognize Parkinson's and healthy patients. This study focuses on examining PD diagnosis through voice data features. A new multi-agent feature filter (MAFT) algorithm is proposed to select the best features from the voice dataset. The MAFT algorithm is designed to select a set of features to improve the overall performance of prediction models and prevent over-fitting possibly due to extreme reduction to the features. Moreover, this algorithm aims to reduce the complexity of the prediction, accelerate the training phase, and build a robust training model. Ten different machine learning methods are then integrated with the MAFT algorithm to form a powerful voice-based PD diagnosis model. Recorded test results of the PD prediction model using the actual and filtered features yielded 86.38% and 86.67% accuracies on average, respectively. With the aid of the MAFT feature selection, the test results are improved by 3.2% considering the hybrid model (HM) and 3.1% considering the Naïve Bayesian and random forest. Subsequently, an HM, which comprises a binary convolutional neural network and three feature selection algorithms (namely, genetic algorithm, Adam optimizer, and mini-batch gradient descent), is proposed to improve the classification accuracy of the PD. The results reveal that PD achieves an overall accuracy of 93.7%. The HM is integrated with the MAFT, and the combination realizes an overall accuracy of 96.9%. These results demonstrate that the combination of the MAFT algorithm and the HM model significantly enhances the PD diagnosis outcomes.

References

  1. S. A. Mostafa, A. Mustapha, M. A. Mohammed, R. I. Hamed, N. Arunkumar, M. K. A. Ghani, M. M. Jaber, and S. H. Khaleefah. 2019. Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson's disease. Cogn. Syst. Res. 54 (2019), 90–99.Google ScholarGoogle ScholarCross RefCross Ref
  2. K. J. Kubota, J. A. Chen, and M. A. Little. 2016. Machine learning for large-scale wearable sensor data in Parkinson's disease: Concepts, promises, pitfalls, and futures. Movement Disorders 31, 9 (2016), 1314–1326.Google ScholarGoogle ScholarCross RefCross Ref
  3. D. Avci and A. Dogantekin. 2016. An expert diagnosis system for Parkinson's disease based on genetic algorithm-wavelet kernel-extreme learning machine. Parkinson's Disease.Google ScholarGoogle Scholar
  4. H. L. Chen, G. Wang, C. Ma, Z. N. Cai, W. B. Liu, and S. J. Wang. 2016. An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson׳ s disease. Neurocomputing 184 (2016), 131–144. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. S. A. Mostafa, A. Mustapha, S. H. Khaleefah, M. S. Ahmad, and M. A. Mohammed. 2018. Evaluating the performance of three classification methods in diagnosis of Parkinson's disease. In Proceedings of the International Conference on Soft Computing and Data Mining. Springer, Cham, 43–52.Google ScholarGoogle Scholar
  6. K. Mueller, R. Jech, and M. L. Schroeter. 2013. Deep-brain stimulation for Parkinson's disease. N. Engl. J. Med. 368, 5 (2013), 482–483.Google ScholarGoogle ScholarCross RefCross Ref
  7. D. Georgiev, M. Domellof, K. Hamberg, L. Forsgren, and G. M. Hariz. 2019. Sex differences, quality of life and non-motor symptoms in Parkinson's disease.Google ScholarGoogle Scholar
  8. A. Rueda, J. C. Vásquez-Correa, C. D. Rios-Urrego, J. R. Orozco-Arroyave, S. Krishnan, and E. Nöth. 2019. Feature representation of pathophysiology of Parkinsonian dysarthria. In Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH’19). 1–5.Google ScholarGoogle Scholar
  9. C. R. Pereira, D. R. Pereira, J. P. Papa, G. H. Rosa, and X. S. Yang. 2016. Convolutional neural networks applied for Parkinson's disease identification. In Machine Learning for Health Informatics. Springer, Cham, 377–390.Google ScholarGoogle Scholar
  10. 2018. Parkinson's Disease-Symptoms, Stages and Life Expectancy, Pathology, Lecturio. Retrieved from https://www.lecturio.com/magazine/parkinsons-disease/.Google ScholarGoogle Scholar
  11. K. H. Abdulkareem, M. A. Mohammed, S. S. Gunasekaran, M. N. Al-Mhiqani, A. A. Mutlag, S. A. Mostafa, N. S. Ali, and D. A. Ibrahim. 2019. A review of fog computing and machine learning: Concepts, applications, challenges, and open issues. IEEE Access 7 (2019), 153123–153140.Google ScholarGoogle ScholarCross RefCross Ref
  12. M. K. Abd Ghani, M. A. Mohammed, N. Arunkumar, S. A. Mostafa, D. A. Ibrahim, M. K. Abdullah, M. M. Jaber, E. Abdulhay, G. Ramirez-Gonzalez, and M. A. Burhanuddin. 2020. Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques. Neural Comput. Appl. 32, 3 (2020), 625–638.Google ScholarGoogle ScholarCross RefCross Ref
  13. M. A. Mohammed, K. H. Abdulkareem, S. A. Mostafa, M. K. A. Ghani, M. S. Maashi, B. Garcia-Zapirain, I. Oleagordia, H. Alhakami, and F. T. AL-Dhief. 2020. Voice pathology detection and classification using convolutional neural network model. Appl. Sciences 10, 11 (2020), 3723.Google ScholarGoogle ScholarCross RefCross Ref
  14. D. Gupta, S. Sundaram, A. Khanna, A. E. Hassanien, and V. H. C. De Albuquerque. 2018. Improved diagnosis of Parkinson's disease using optimized crow search algorithm. Comput. Electric. Eng. 68 (2018), 412–424.Google ScholarGoogle ScholarCross RefCross Ref
  15. D. Gupta, A. Julka, S. Jain, T. Aggarwal, A. Khanna, N. Arunkumar, and V. H. C. de Albuquerque. 2018. Optimized cuttlefish algorithm for diagnosis of Parkinson's disease. Cogn. Syst. Res. 52 (2018), 36–48.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. K. Wrobel. 2019. Diagnosing Parkinson's disease with the use of a reduced set of patients’ voice features samples. In Proceedings of the IFIP International Conference on Computer Information Systems and Industrial Management. Springer, Cham, 84–95.Google ScholarGoogle ScholarCross RefCross Ref
  17. H. Gürüler. 2017. A novel diagnosis system for Parkinson's disease using complex-valued artificial neural network with k-means clustering feature weighting method. Neural Comput. Applications 28, 7 (2017), 1657–1666. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. A. Ul Haq, J. Li, Z. Ali, M. H. Memon, M. Abbas, and S. Nazir. Recognition of the Parkinson's disease using a hybrid feature selection approach. J. Intell. Fuzzy Syst. 1–21.Google ScholarGoogle Scholar
  19. M. Little, P. McSharry, E. Hunter, J. Spielman, and L. Ramig. 2008. Suitability of dysphonia measurements for telemonitoring of Parkinson's disease. IEEE Trans. Biomed. Eng. 56, 4 (2009).Google ScholarGoogle Scholar
  20. A. G. Ramayya, A. Misra, G. H. Baltuch, and M. J. Kahana. 2014. Microstimulation of the human substantia nigra alters reinforcement learning. J. Neurosci. 34, 20 (2014), 6887–6895.Google ScholarGoogle ScholarCross RefCross Ref
  21. M. Can. 2013. Neural networks to diagnose the Parkinson's disease. Southeast Eur. J. Soft Comput. 2, 1 (2013).Google ScholarGoogle Scholar
  22. A. Tsanas, M. A. Little, P. E. McSharry, and L. O. Ramig. 2009. Accurate telemonitoring of Parkinson's disease progression by noninvasive speech tests. IEEE Trans. Biomed. Eng. 57, 4 (2009), 884–893.Google ScholarGoogle ScholarCross RefCross Ref
  23. E. Kaya, O. Findik, I. Babaoglu, and A. Arslan. 2011. Effect of discretization method on the diagnosis of Parkinson's disease. Int. J. Innov. Comput. Info. 7 (2011), 4669–4678.Google ScholarGoogle Scholar
  24. M. Hariharan, K. Polat, and R. Sindhu. 2014. A new hybrid intelligent system for accurate detection of Parkinson's disease. Comput. Methods Programs Biomed. 113, 3 (2014), 904–913. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. S. Doan and S. Horiguchi. 2004. An agent-based approach to feature selection in text categorization. In Proceedings of 2nd International Conference on Autonomous Robot and Agent. 362–366.Google ScholarGoogle Scholar
  26. F. Farahnakian and N. Mozayani. 2009. Evaluating feature selection techniques in simulated soccer multi-agents system. In Proceedings of the International Conference on Advanced Computer Control. IEEE, 107–110. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. M. S. P. Subathra, M. A. Mohammed, M. S. Maashi, B. Garcia-Zapirain, N. J. Sairamya, and S. T. George. 2020. Detection of focal and non-focal electroencephalogram signals using fast Walsh-Hadamard transform and artificial neural network. Sensors 20, 17 (2020), 4952.Google ScholarGoogle ScholarCross RefCross Ref
  28. M. A. Mohammed, K. H. Abdulkareem, A. S. Al-Waisy, S. A. Mostafa, S. Al-Fahdawi, A. M. Dinar, W. Alhakami, A. Baz, M. N. Al-Mhiqani, H. Alhakami, and N. Arbaiy. 2020. Benchmarking methodology for selection of optimal COVID-19 diagnostic model based on entropy and TOPSIS methods. IEEE Access.Google ScholarGoogle Scholar
  29. M. A. Mohammed, M. K. A. Ghani, R. I. Hamed, S. A. Mostafa, D. A. Ibrahim, H. K. Jameel, and A. H. Alallah. 2017. Solving vehicle routing problem by using improved K-nearest-neighbor algorithm for best solution. J. Comput. Sci. 21 (2017), 232–240.Google ScholarGoogle ScholarCross RefCross Ref
  30. M. A. Mohammed, B. Al-Khateeb, A. N. Rashid, D. A. Ibrahim, M. K. A. Ghani, and S. A. Mostafa. 2018. Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images. Comput. Electric. Eng. 70 (2018), 871–882.Google ScholarGoogle ScholarCross RefCross Ref
  31. C. I. Sánchez, R. Hornero, A. Mayo, and M. García. 2009. Mixture model-based clustering and logistic regression for automatic detection of microaneurysms in retinal images. In Medical Imaging 2009: Computer-Aided Diagnosis, Vol. 7260. International Society for Optics and Photonics, 72601M.Google ScholarGoogle Scholar
  32. N. Arunkumar, M. A. Mohammed, S. A. Mostafa, D. A. Ibrahim, J. J. Rodrigues, and V. H. C. de Albuquerque. 2020. Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks. Concurr. Comput.: Pract. Exper. 32, 1 (2020), e4962.Google ScholarGoogle ScholarCross RefCross Ref
  33. N. Arunkumar, M. A. Mohammed, M. K. A. Ghani, D. A. Ibrahim, E. Abdulhay, G. Ramirez-Gonzalez, and V. H. C. de Albuquerque. 2019. K-means clustering and neural network for object detecting and identifying abnormality of brain tumor. Soft Comput. 23, 19 (2019), 9083–9096.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. C. Ying, M. Qi-Guang, L. Jia-Chen, and G. Lin. 2013. Advance and prospects of AdaBoost algorithm. Acta Automatica Sinica 39, 6 (2013), 745–758.Google ScholarGoogle ScholarCross RefCross Ref
  35. J. Van Zyl and I. Cloete. 2004. FuzzConRI—A fuzzy conjunctive rule inducer. In Proceedings of the Workshop on Advances in Inductive Rule Learning (ECML’04). 194–203.Google ScholarGoogle Scholar
  36. M. Zinkevich, M. Weimer, L. Li, and A. J. Smola. 2010. Parallelized stochastic gradient descent. In Advances in Neural Information Processing Systems. MIT Press, 2595–2603. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. J. Mekyska, Z. Galaz, Z. Mzourek, Z. Smekal, I. Rektorova, I. Eliasova, M. Kostalova, M. Mrackova, D. Berankova, M. Faundez-Zanuy, and K. López-de-Ipina. 2015. Assessing progress of Parkinson's disease using acoustic analysis of phonation. In Proceedings of the 4th International Work Conference on Bioinspired Intelligence (IWOBI’15). IEEE, 111–118.Google ScholarGoogle Scholar
  38. T. T. Wong. 2015. Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recogn. 48, 9 (2015), 2839–2846. Google ScholarGoogle ScholarDigital LibraryDigital Library

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