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Speech-Based Parkinson’s Disease Prediction Using XGBoost-Based Features Selection and the Stacked Ensemble of Classifiers

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Abstract

Parkinson’s disease (PD) is a neuron-related disorder due to the decrease in dopaminergic neurons present in the midbrain. For the last few decades, speech is an emerging interest in the analysis and detection of PD. In this study, a predictive machine learning framework based on extreme gradient boosting (XGBoost) features selection and a stacked ensemble approach is presented to investigate the voice tremor of people suffering from PD. The proposed framework consists of two stages: In the first stage the optimized features are obtained using XGBoost features selection, and in the second stage a PD detection system is developed using stacked ensemble classifiers. Leave one subject out (LOSO) cross-validation shows that the proposed framework gives average accuracy of up to 95.07% compared to results obtained with individual classifiers. Additionally, it was also concluded that reduced features had given the highest classification accuracy compared to the raw features set which saves training time and enhances the prediction accuracy.

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References

  1. K Dashtipour A Tafreshi J Lee B Crawley 2018 Speech disorders in Parkinson's disease: pathophysiology, medical management and surgical approaches Neurodegener. Dis. Manag. 8 5 337 348

    Article  Google Scholar 

  2. A Roberts D Post 2018 Information content and efficiency in the spoken discourse of individuals with Parkinson's disease J. Speech Lang. Hear. Res. 61 9 2259 2274

    Article  Google Scholar 

  3. J Mühlhaus H Frieg K Bilda U Ritterfeld 2017 Game-based speech rehabilitation for people with Parkinson’s disease Lect. Notes Comput. Sci. 10279 76 85

    Article  Google Scholar 

  4. MA Little PE McSharry EJ Hunter J Spielman LO Ramig 2009 Suitability of dysphonia measurements for telemonitoring of Parkinson's disease IEEE Trans. Biomed. Eng. 56 4 1015 1022

    Article  Google Scholar 

  5. J Rusz R Čmejla H Ruzickova E Růžička 2011 Quantitative acoustic measurements for characterization of speech and voice disorders in early untreated Parkinson’s disease J. Acoust. Soc. Am. 129 1 350 367

    Article  Google Scholar 

  6. A Tsanas MA Little PE McSharry J Spielman LO Ramig 2012 Novel speech signal processing algorithms for high accuracy classification of Parkinsons disease IEEE Trans. Biomed. Eng. 59 1264 1271

    Article  Google Scholar 

  7. S Lahmiri A Shmuel 2019 Detection of Parkinson’s disease based on voice patterns ranking and optimized support vector machine Biomed. Signal Process. Control 49 427 433

    Article  Google Scholar 

  8. M Novotný J Rusz R Čmejla E Růžička 2014 Automatic evaluation of articulatory disorders in Parkinson’s disease IEEE/ACM Trans. Audio Speech Lang. Process. 22 9 1366 1378

    Article  Google Scholar 

  9. JR Orozco-Arroyave JD Arias-Londoño JF Vargas-Bonilla E Nöth 2013 Analysis of speech from people with Parkinson’s disease through nonlinear dynamics Lect. Notes Comput. Sci. 7911 112 119

    Article  Google Scholar 

  10. BE Sakar ME Isenkul CO Sakar A Sertbas F Gurgen S Delil H Apaydin O Kursun 2013 Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings IEEE J. Biomed. Health Inform. 17 828 834

    Article  Google Scholar 

  11. JR Orozco-Arroyave F Hönig JD Arias-Londoño JF Vargas-Bonilla E Nöth 2015 Spectral and cepstral analyses for Parkinson’s disease detection in Spanish vowels and words Expert. Syst. 32 688 697

    Article  Google Scholar 

  12. T Khan J Westin M Dougherty 2014 Cepstral separation difference: A novel approach for speech impairment quantification in Parkinson’s disease Biocybern. Biomed. Eng. 34 1 25 34

    Article  Google Scholar 

  13. Tsanas, Athanasios, et al. Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests. Nat. Preced.  1–1 (2009)

  14. P Gómez-Vilda J Mekyska JM Ferrández D Palacios-Alonso A Gómez-Rodellar V Rodellar-Biarge Z Galaz Z Smekal I Eliasova M Kostalova I Rektorova 2017 Parkinson’s disease detection from speech articulation neuromechanics Front. Neuroinform. 11 1 17

    Article  Google Scholar 

  15. C. Okan Sakar 2019 A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform Appl. Soft Comput. 74 255 263

    Article  Google Scholar 

  16. B Karan SS Sahu K Mahto 2020 Parkinson disease prediction using intrinsic mode function based features from speech signal Biocybern. Biomed. Eng. 40 1 249 264

    Article  Google Scholar 

  17. B Karan SS Sahu 2021 An improved framework for Parkinson’s disease prediction using variational mode decomposition-hilbert spectrum of speech signal Biocybern. Biomed. Eng. 41 717 732

    Article  Google Scholar 

  18. A Tsanas 2012 Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease IEEE Trans. Biomed. Eng. 59 5 1264 1271

    Article  Google Scholar 

  19. G Solana-Lavalle J-C Galán-Hernández R Rosas-Romero 2020 Automatic Parkinson disease detection at early stages as a pre-diagnosis tool by using classifiers and a small set of vocal features Biocybern. Biomed. Eng. 40 1 505 516

    Article  Google Scholar 

  20. G Solana-Lavalle R Rosas-Romero 2021 Analysis of voice as an assisting tool for detection of Parkinson's disease and its subsequent clinical interpretation Biomed. Signal Process. Control 66 102415

    Article  Google Scholar 

  21. H Gunduz 2021 An efficient dimensionality reduction method using filter-based feature selection and variational autoencoders on Parkinson's disease classification Biomed. Signal Process. Control 66 102452

    Article  Google Scholar 

  22. H Gunduz 2019 Deep learning-based Parkinson’s disease classification using vocal feature sets IEEE Access 7 115540 115551

    Article  Google Scholar 

  23. D Gupta S Sundaram A Khanna AE Hassanien VHC Albuquerque De 2018 Improved diagnosis of Parkinson's disease using optimized crow search algorithm Comput. Electr. Eng. https://doi.org/10.1016/j.compeleceng.2018.04.014

    Article  Google Scholar 

  24. D Gupta A Julka S Jain T Aggarwal A Khanna N Arunkumar VHC Albuquerque de 2018 Optimized cuttlefish algorithm for diagnosis of Parkinson’s disease Cogn. Syst. Res. https://doi.org/10.1016/j.cogsys.2018.06.006

    Article  Google Scholar 

  25. Q Zhang 2021 StackPDB: predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier Appl. Soft Comput. 99 106921

    Article  Google Scholar 

  26. DH Wolpert 1992 Stacked generalization Neural Netw. 5 2 241 259

    Article  Google Scholar 

  27. TG Dietterich 1998 Approximate statistical tests for comparing supervised classification learning algorithms Neural Comput. 10 7 1895 1923

    Article  Google Scholar 

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  1. Department of Electronics and Communication Engineering, Aditya Engineering College(A) , Surampalem, Andhra Pradesh, India

    Biswajit Karan

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Karan, B. Speech-Based Parkinson’s Disease Prediction Using XGBoost-Based Features Selection and the Stacked Ensemble of Classifiers. J. Inst. Eng. India Ser. B 104, 475–483 (2023). https://doi.org/10.1007/s40031-022-00851-2

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