An Ensemble Learning Driven Voice Investigation for Early Screening of Parkinson's Disease
Keywords:
Parkinson's disease (PD), Machine Learning (ML), Baseline features, fundamental frequency features, Naive Bayes (NB), Random Forest (RF), K-Nearest Neighbors (KNN), Gradient Boosting (GB), XGBoost (XGB).Abstract
Parkinson's disease (PD) is a complex and prevalent central nervous syndrome characterized by the emergence of unintended or uncontrollable movements, accompanied by symptoms. The global prevalence of PD has escalated to an estimated 9.4 million individuals, indicating a substantial rise from 6 million. This alarming surge underscores the urgent need for proactive measures to address the growing burden of this neurodegenerative disease and its profound impact on society. The machine learning algorithms used on PD dataset are designed to find the optimum approach for examining the seriousness of the Parkinson disease. The primary focus of this research centers on employing machine learning algorithms to enhance our ability for early prediction of Parkinson's disease accurately. This research attempts to find the best model by investigating a wide variety of classification algorithms. The objective is to enhance early identification and diagnosis, therefore improving patient outcomes and lowering the load on healthcare providers. For this purpose, we have opted various Machine Learning algorithms such as the NB, RF, KNN, GB and XGboost. The evaluation of each algorithm's performance is meticulously conducted through rigorous experimentation, taking into account metrics such as Jaccard similarity, precision, sensitivity, F1score and accuracy. The dataset utilized in this study encompasses valuable clinical and demographic information of PD patients, which enables us to develop and train the mentioned algorithms. We demonstrated the Gradient boosting (GB) algorithm's significant performance across all machine learning algorithms to predict patients with PD. The results are encouraging and reveal the potential for ML algorithms to accurately and efficiently predict symptoms that are undetectable to a medical professional.
Downloads
References
Filippo Cavallo , Alessandra Moschetti , Dario Esposito, Carlo Maremmani, Erika Rovini : Upper limb motor pre-clinical assessment in Parkinson's disease using machine learning , Received 24 September 2018, Revised 19 February 2019, Accepted 19 February 2019, https://doi.org/10.1016/j.parkreldis.2019.02.028
Kamal Nayan Reddy Challa; Venkata Sasank Pagolu; Ganapati Panda; Babita Majhi : An improved approach for prediction of Parkinson's disease using machine learning techniques , 26 June 2017 , doi : 10.1109/SCOPES.2016.7955679
Raya Alshammri, Ghaida Alharbi, Ebtisam Alharbi and Ibrahim Almubark: machine learning approaches to identify parkinson’s disease using voice signal features , 28 march 2023 , 10.3389/frai.2023.1084001.
Prof. W. Poewe, Department of Neurology, Medical University of Innsbruck, Anichstrasse : non motor symptoms in parkinson's disease, 17 march 2008, https://doi.org/10.1111/j.1468-1331.2008.02056.x
Fatemeh N. Emamzadeh , Andrei Surguchov : parkinson's diseases : biomarkers , treatments and risk factors, https://doi.org/10.3389/fnins.2018.00612
C. Quan, K. Ren and Z. Luo, "A Deep Learning Based Method for Parkinson’s Disease Detection Using Dynamic Features of Speech," in IEEE Access, vol. 9, pp. 10239-10252, 2021, doi: 10.1109/ACCESS.2021.3051432.
K. M. Tsiouris et al., "Predicting rapid progression of Parkinson's Disease at baseline patients evaluation," 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Korea (South), 2017, pp. 3898-3901, doi: 10.1109/EMBC.2017.8037708.
Basil K Varghese, Geraldine Bessie Amali D, Uma Devi K S. Prediction of Parkinson’s Disease using Machine Learning Techniques on Speech dataset. Research J. Pharm. and Tech 2019; 12(2):644-648. doi: 10.5958/0974-360X.2019.00114.8
Moshkova, A. Samorodov, N. Voinova, A. Volkov, E. Ivanova and E. Fedotova, "Parkinson’s Disease Detection by Using Machine Learning Algorithms and Hand Movement Signal from LeapMotion Sensor," 2020 26th Conference of Open Innovations Association (FRUCT), Yaroslavl, Russia, 2020, pp. 321-327, doi: 10.23919/FRUCT48808.2020.9087433.
WU WANG , JUNHO LEE , FOUZI HARROU, AND YING SUN Early Detection of Parkinson’s Disease Using Deep Learning and Machine Learning IEEE Access DoI 10.1109/ACCESS.2020.3016062
D. V. Rao, Y. Sucharitha, D. Venkatesh, K. Mahamthy and S. M. Yasin, "Diagnosis of Parkinson's Disease using Principal Component Analysis and Machine Learning algorithms with Vocal Features," 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, 2022, pp. 200-206, doi: 10.1109/ICSCDS53736.2022.9760962.
P. Mounika and S. G. Rao, "Machine Learning and Deep Learning Models for Diagnosis of Parkinson’s Disease: A Performance Analysis," 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 2021, pp. 381-388, doi: 10.1109/I-SMAC52330.2021.9640632.
Yolanda Campos-Roca , Fernando Calle-Alonso, Carlos J. Perez, Lizbeth Naranjo Computational diagnosis of Parkinson’s Disease from speech based on regularization methods 2018 26th European Signal Processing Conference (EUSIPCO) ISBN 978-90-827970-1-5 © EURASIP 2018
Max A. Little , Declan A.E. Costello , Meredydd L. Harries Objective dysphonia quantification in vocal fold 2 paralysis: comparing nonlinear with classical measures Nature Precedings : hdl:10101/npre.2009.3106.1 : Posted 20 Apr 2009
Max Wang , Wenbo Ge , Deborah Apthorp, Hanna Suominen Robust Feature Engineering for Parkinson Disease Diagnosis: New Machine Learning Techniques JMIR BIOMEDICAL ENGINEERING JMIR Biomed Eng 2020 | vol. 5 | iss. 1 | e13611 | p. 1-13
Yunfeng Wu,1 Pinnan Chen,1 Yuchen Yao,1 Xiaoquan Ye,1 Yugui Xiao,1 Lifang Liao,1 Meihong Wu,1 and Jian Chen Dysphonic Voice Pattern Analysis of Patients in Parkinson’s Disease Using Minimum Interclass Probability Risk Feature Selection and Bagging Ensemble Learning Methods Hindawi Computational and Mathematical Methods in Medicine Volume 2017, Article ID 4201984, 11 pages https://doi.org/10.1155/2017/4201984
Juan Rafael Orozco-Arroyave , Julian David Arias-Londo , Jesus Francisco Vargas-Bonilla , and Elmar Noth Analysis of Speech from People with Parkinson’s Disease through Nonlinear Dynamics T. Drugman and T. Dutoit (Eds.): NOLISP 2013, LNAI 7911, pp. 112–119, 2013. c Springer-Verlag Berlin Heidelberg 2013
Sakar, C.O., Serbes, G., Gunduz, A., Tunc, H.C., Nizam, H., Sakar, B.E., Tutuncu, M., Aydin, T., Isenkul, M.E. and Apaydin, H., 2018. 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, DOI: [Web Link] https://doi.org/10.1016/j.asoc.2018.10.022
Nolte G, Aburidi M, Engel AK. Robust calculation of slopes in detrended fluctuation analysis and its application to envelopes of human alpha rhythms. Sci Rep. 2019 Apr 19;9(1):6339. doi: 10.1038/s41598-019-42732-7. PMID: 31004085; PMCID: PMC6474881.
Max A. Little , Declan A.E , Meredydd L. Harries Objective dysphonia quantification in vocal fold 2 paralysis: comparing nonlinear with classical measures Nature Precedings : hdl:10101/npre.2009.3106.1 : Posted 20 Apr 2009
Karan, Biswajit and Sahu, Sitanshu Sekhar (2022) "Factor Analysis of Speech Signal for Parkinson’s Disease Prediction using Support Vector Machine," International Journal of Electronics and Electical Engineering: Vol. 4 : Iss. 1 , Article 3. DOI: 10.47893/IJEEE.2022.1181
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
