Hybrid Neuro-Explainable Ensemble Framework for Early Detection of Parkinson’s Disease Using Speech-Based Acoustic Features

Early-Onset Parkinson’s Disease (PD) is a major clinical concern due to the continuous neurological degeneration and slight prodromal symptoms. The degeneration of dopaminergic neurons in patients with Parkinson’s Disease directly influences both motor and vocal activities. The acoustic degradation in patients’ voices is more pronounced than other motor activities. Vocal parameters such as jitter, shimmer, and harmonics to noise ratio have exhibited high potential for early Parkinson’s Disease detection. However, supervised learning models such as Logistic Regression and Gradient Boosting are unable to capture the non-linear variability in pathological speech. In this regard, a novel framework known as the Hybrid Neuro-Explainable Ensemble Framework (HNEF) is proposed. The framework integrates two supervised learning models, namely Regularized Logistic Regression and Gradient Boosting, using a weighted soft voting approach. The framework can capture linear and non-linear decision boundaries. Moreover, a novel hybrid oversampling technique is incorporated to tackle the common class imbalance in Parkinson’s Disease datasets. The technique combines K-Means-based synthetic minority oversampling and density-sensitive oversampling. The relevance of features is determined via a sequential two-stage pipeline consisting of Recursive Feature Elimination and Mutual Information scoring, thus ensuring the preservation of the most diagnostically relevant vocal features. The prediction pipeline incorporates features of interpretability via the SHAP-based global attribution and the attention mechanism, thus ensuring accountability in the telemedicine and AI-assisted clinical settings. The experimental results, considering the standard PD speech dataset, show the efficacy of HNEF with a classification accuracy of 97.8%, an F1-score of 97.1%, and an AUC-ROC value of 0.99, thus outperforming all the individual baseline models, including SVM, Random Forest, XGBoost, and deep neural networks. The ten-fold cross-validation results show the robustness of the findings with a high accuracy of 97.5% ± 0.5%. The potential of HNEF will be explored for the extension of the system to speech monitoring for tracking disease progression, its integration with multimodal biomarkers including gait and EEG, and its prospective validation with demographically diverse patients for achieving regulatory approval.

Parkinson’s Disease, Speech Biomarkers

1. M. A. Little, P. E. McSharry, S. J. Roberts, D. A. Costello, and I. M. Moroz, “Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection,” BioMed. Eng. OnLine, vol. 6, no. 1, p. 23, Jun. 2007, doi: 10.1186/1475-925X-6-23. [Google Scholar] [Crossref]

2. B. E. Sakar, M. E. Isenkul, C. O. Sakar, A. Sertbas, F. Gurgen, S. Delil, H. Apaydin, and O. Kursun, “Collection and Analysis of a Parkinson Speech Dataset with Multiple Types of Sound Recordings,” IEEE J. Biomed. Health Inform., vol. 17, no. 4, pp. 828–834, Jul. 2013, doi: 10.1109/JBHI.2013.2245674. [Google Scholar] [Crossref]

3. A. Tsanas, M. A. Little, P. E. McSharry, and L. O. Ramig, “Accurate Telemonitoring of Parkinson’s Disease Progression by Noninvasive Speech Tests,” IEEE Trans. Biomed. Eng., vol. 57, no. 4, pp. 884–893, Apr. 2010, doi: 10.1109/TBME.2009.2036000. [Google Scholar] [Crossref]

4. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-Sampling Technique,” J. Artif. Intell. Res., vol. 16, pp. 321–357, Jun. 2002, doi: 10.1613/jair.953. [Google Scholar] [Crossref]

5. S. M. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” in Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 30, Long Beach, CA, USA, Dec. 2017, pp. 4765–4774. [Google Scholar] [Crossref]

6. I. Guyon and A. Elisseeff, “An Introduction to Variable and Feature Selection,” J. Mach. Learn. Res., vol. 3, pp. 1157–1182, Mar. 2003. [Google Scholar] [Crossref]

7. J. H. Friedman, “Greedy Function Approximation: A Gradient Boosting Machine,” Ann. Statist., vol. 29, no. 5, pp. 1189–1232, Oct. 2001, doi: 10.1214/aos/1013203451. [Google Scholar] [Crossref]

8. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 30, Long Beach, CA, USA, Dec. 2017, pp. 5998–6008. [Google Scholar] [Crossref]

9. R. B. Postuma, D. Berg, M. Stern, W. Poewe, C. W. Olanow, W. Oertel, J. Obeso, K. Marek, I. Litvan, A. E. Lang, G. Halliday, C. G. Goetz, T. Gasser, B. Dubois, P. Chan, B. R. Bloem, C. H. Adler, and G. Deuschl, “MDS Clinical Diagnostic Criteria for Parkinson’s Disease,” Mov. Disord., vol. 30, no. 12, pp. 1591–1601, Oct. 2015, doi: 10.1002/mds.26424. [Google Scholar] [Crossref]

10. Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015, doi: 10.1038/nature14539. [Google Scholar] [Crossref]

11. M. Thimmapuram, A. R. Akepogu, and P. R. Raju, "Analysis of Freezing of Gait in Parkinson's Disease Detection Using a Multimodal Prototype Learning Framework," IEEE Trans. Neural Syst. Rehabil. Eng., 2025, doi: 10.1109/TNSRE.2025.3605204. [Google Scholar] [Crossref]

12. R. Souza et al., "Identifying Biases in a Multicenter MRI Database for Parkinson's Disease Classification: Is the Disease Classifier a Secret Site Classifier?," IEEE J. Biomed. Health Inform., 2024, doi: 10.1109/JBHI.2024.3352513. [Google Scholar] [Crossref]

13. V. Skaramagkas, A. Pentari, Z. Kefalopoulou, and M. Tsiknakis, "Multi-Modal Deep Learning Diagnosis of Parkinson's Disease — A Systematic Review," IEEE Trans. Neural Syst. Rehabil. Eng., 2023, doi: 10.1109/TNSRE.2023.3277749. [Google Scholar] [Crossref]

14. A. Nogales et al., "BERT Learns From Electroencephalograms About Parkinson's Disease: Transformer-Based Models for Aid Diagnosis," IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3201843. [Google Scholar] [Crossref]

15. P. Khan et al., "Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances," IEEE Access, 2021, doi: 10.1109/ACCESS.2021.3062484. [Google Scholar] [Crossref]

16. Y. Tang, Y. Yuan, F. Tao, and M. Tang, "Cross-Modal Augmented Transformer for Automated Medical Report Generation," IEEE J. Transl. Eng. Health Med., 2025, doi: 10.1109/JTEHM.2025.3536441. [Google Scholar] [Crossref]

17. Y. Fu et al., "Design and Implementation of a Medical Device Data Dictionary System Based on Software Engineering Theory," IEEE Access, 2024, doi: 10.1109/ACCESS.2024.3442372. [Google Scholar] [Crossref]

18. T. Melvin, "The European Medical Device Regulation — What Biomedical Engineers Need to Know," IEEE J. Transl. Eng. Health Med., 2022, doi: 10.1109/JTEHM.2022.3194415. [Google Scholar] [Crossref]

19. D. L. Guarín, J. K. Wong, N. R. McFarland, and A. Ramirez-Zamora, "Characterizing Disease Progression in Parkinson's Disease from Videos of the Finger Tapping Test," IEEE Trans. Neural Syst. Rehabil. Eng., 2024, doi: 10.1109/TNSRE.2024.3416446. [Google Scholar] [Crossref]

20. Z. Qu, T. Yao, X. Liu, and G. Wang, "A Graph Convolutional Network Based on Univariate Neurodegeneration Biomarker for Alzheimer's Disease Diagnosis," IEEE J. Transl. Eng. Health Med., 2023, doi: 10.1109/JTEHM.2023.3285723. [Google Scholar] [Crossref]

21. D. D. Hema and T. R. Jaison, “A Novel Deep Learning-Driven Smart System for Lane Change Decision-Making,” Int. J. Intell. Transp. Syst. Res., vol. 22, no. 3, pp. 648–659, 2024. [Google Scholar] [Crossref]

22. Deva Hema, D., Ashok Kumar, K.: Levenberg–Marquardt –LSTM based Efficient Rear‑end Crash Risk Prediction System Optimization. Int. J. Intell. Transp. Syst. Res. 20, 132–141 (2021). [Google Scholar] [Crossref]

• Measuring Waste of Patient Time in Health Care at Non-Digitized Hospital: An Observational Study in Bangabandhu Sheikh Mujib Medical University, Bangladesh
• Reaffirming Clinical Confidence in Atorvastatin Therapy: A Digital Outreach Case Study from Tamil Nadu, India
• Clinical Manifestations and Therapeutic Response in a Patient with Hypothyroidism: A Case Report
• Eranda (Ricinus Communis) In Gridhrasi (Sciatica): Classical Rationale, Pharmacology and Clinical Evidence- A Narrative Literature Review
• Magnetotherapy in Pain Management: Mechanisms, Clinical Applications, and Future Perspectives – A Review