An Ensemble Method for Diagnosis of Parkinson's Disease Based on Voice Measurements
DOI: 10.4103/jmss.JMSS_57_18
Abstract
Background: Parkinson's disease (PD) is the most common destructive neurological disorder after Alzheimer's disease. Unfortunately, there is no specific test such as electroencephalography or blood test for diagnosing the disease. In accordance with the previous studies, about 90% of people with PD have some types of voice abnormalities. Therefore, voice measurements can be used to detect the disease. Methods: This study presents an ensemble-based method for identifying patients and healthy samples by class label prediction based on voice frequency characteristics. It includes three stages of data preprocessing, internal classification and ultimate classification. The outcomes of internal classifiers next to primary feature vector of samples are considered the ultimate classifier inputs. Results: According to the results, the proposed method achieved 90.6% of accuracy, 95.8% of sensitivity, and 75% of specificity, admissible compared to those of other relevant studies. Conclusion: Current experimental outcomes provide a comparative analysis of various machine learning classifiers and confirm that using ensemble-based methods has improved medical diagnostic tasks.
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DeMaagd G, Philip A. Parkinson's disease and its management: Part 1: Disease entity, risk factors, pathophysiology, clinical presentation, and diagnosis. P T 2015;40:504-32.
Coomber R, Alshameeri Z, Masia AF, Mela F, Parker MJ. Hip fractures and Parkinson's disease: A case series. Injury 2017;48:2730-5.
Müller T. Drug therapy in patients with Parkinson's disease. Transl Neurodegener 2012;1:10.
Singh N, Pillay V, Choonara YE. Advances in the treatment of Parkinson's disease. Prog Neurobiol 2007;81:29-44.
Gupte C, Gadewar S. Diagnosis of Parkinson's disease using acoustic analysis of voice. Int J Sci Res Netw Secur Communication 2017;5:14-18.
Baken RJ, Orlikoff RF. Clinical Measurement of Speech and Voice. 2nd ed. US: Cengage Learning; 1999.
Dejonckere PH, Bradley P, Clemente P, Cornut G, Crevier-Buchman L, Friedrich G, et al. Abasic protocol for functional assessment of voice pathology, especially for investigating the efficacy of (phonosurgical) treatments and evaluating new assessment techniques. Guideline elaborated by the committee on phoniatrics of the European Laryngological Society (ELS). Eur Arch Otorhinolaryngol 2001;258:77-82.
Sakar BE, Isenkul ME, Sakar CO, Sertbas A, Gurgen F, Delil S, et al. Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J Biomed Health Inform 2013;17:828-34.
Zhang HH, Yang L, Liu Y, Wang P, Yin J, Li Y, et al. Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples. Biomed Eng Online 2016;15:122.
Tsanas A, Little MA, McSharry PE, Ramig LO. Accurate telemonitoring of Parkinson's disease progression by non-invasive speech tests. IEEE J Biomed Health Inform 2009;57:884-93.
Gil D, Johnson M. Diagnosing Parkinson by using artificial neural networks and support vector machines. Global J Comput Sci Tech 2009;9:63-71.
Ene M. Neural network-based approach to discriminate healthy people from those with Parkinson's disease. Comput Sci Ser 2008;35:112-6.
Ullah Khan S. Classification of Parkinson's disease using data mining techniques. J Parkinsons Dis Alzheimer Dis 2015;2:4.
Khemphila A, Boonjing V. Parkinson's disease classification using neural network and feature selection. Int J Math Comput Sci 2012;6:377-80.
Ozcift A, Gulten A. Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Comput Methods Programs Biomed 2011;104:443-51.
Little MA, McSharry PE, Roberts SJ, Costello DA, Moroz IM. Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. Biomed Eng Online 2007;6:23.
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