{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:02:12Z","timestamp":1785420132736,"version":"3.56.0"},"reference-count":23,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T00:00:00Z","timestamp":1778371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010739","name":"University of Camerino","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100010739","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/0005w8d69","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neurology International"],"abstract":"<jats:p>Background: Parkinson\u2019s disease (PD) is a progressive neurodegenerative disorder that affects millions globally, particularly those in the elderly population. Several occupational exposures typical of maritime environments are recognized or suspected risk factors for PD, warranting attention within occupational health frameworks. The disease is characterized by motor symptoms such as tremor, rigidity, and bradykinesia, as well as non-motor impairments including speech abnormalities. Objective: Early diagnosis is crucial for effective disease management but remains challenging due to symptoms overlapping with normal aging and other neurological conditions. This study presents a machine learning (ML)-based approach for the early diagnosis of PD using speech signal analysis. Methods: We employed six supervised ML classifiers to differentiate between PD patients and healthy controls based on vocal features. The experimental dataset, MDVR-KCL, consists of speech recordings from both reading tasks and spontaneous dialogs, collected via mobile devices. From these recordings, we extracted Mel-Frequency Cepstral Coefficients (MFCCs), Gammatone Frequency Cepstral Coefficients (GTCCs), and acoustic features such as jitter, shimmer, and harmonic-to-noise ratio. These features capture a broad range of prosodic, spectral, and articulatory characteristics associated with PD-related speech impairments. Speaker diarization was applied in spontaneous dialog recordings to separate participant speech. Hyperparameter tuning was performed using GridSearchCV with 10-fold cross-validation, while final model evaluation was conducted using Leave-One-Subject-Out Cross-Validation (LOSOCV) to ensure subject-independent performance assessment. Results: In the read-text task, the SVM model performed exceptionally, yielding 95.45% accuracy, 94.62% sensitivity, 95.97% specificity, an F1-score of 94.12%, and an AUC of 0.98 with an MCC value of 0.90, for GTCCs with the acoustic features. In the spontaneous dialog task, the XGB model demonstrated the highest overall performance across all metrics, with a test accuracy of 83.7%, a sensitivity of 76.3.9%, a specificity of 88.9%, an F1-score of 79.5%, an AUC value of 0.88, and an MCC value of 0.66. Conclusions: Comparable results were obtained on both spontaneous dialog and reading speech subsets, demonstrating the robustness of the approach across different speaking contexts. These results demonstrate the effectiveness of integrating cepstral and acoustic features with machine learning models for non-invasive PD classification. The findings support the use of speech-based digital biomarkers in early PD detection and highlight the potential for developing scalable tools. This work highlights the potential of speech-based digital diagnostics to support clinical decision-making and improve patient outcomes.<\/jats:p>","DOI":"10.3390\/neurolint18050088","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T06:42:18Z","timestamp":1779691338000},"page":"88","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Approaches to Early Detection of Parkinson\u2019s Disease Using Speech Analysis Technique"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2585-9260","authenticated-orcid":false,"given":"Mohammad Amran","family":"Hossain","sequence":"first","affiliation":[{"name":"Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5275-4099","authenticated-orcid":false,"given":"Enea","family":"Traini","sequence":"additional","affiliation":[{"name":"Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0555-1034","authenticated-orcid":false,"given":"Francesco","family":"Amenta","sequence":"additional","affiliation":[{"name":"Research Department, International Radio Medical Centre (C.I.R.M.), 00144 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106078","DOI":"10.1016\/j.parkreldis.2024.106078","article-title":"Towards a Biological Diagnosis of PD","volume":"122","author":"Chopra","year":"2024","journal-title":"Park. Relat. Disord."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jeancolas, L., Petrovska-Delacr\u00e9taz, D., Mangone, G., Benkelfat, B.-E., Corvol, J.-C., Vidailhet, M., Leh\u00e9ricy, S., and Benali, H. (2021). X-Vectors: New Quantitative Biomarkers for Early Parkinson\u2019s Disease Detection from Speech. Front. Neuroinform., 15.","DOI":"10.3389\/fninf.2021.578369"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/2047-9158-1-11","article-title":"Premotor Biomarkers for Parkinson\u2019s Disease\u2014A Promising Direction of Research","volume":"1","author":"Haas","year":"2012","journal-title":"Transl. Neurodegener."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2283","DOI":"10.1093\/brain\/114.5.2283","article-title":"Ageing and Parkinson\u2019s disease: Substantia nigra regional selectivity","volume":"114","author":"Fearnley","year":"1991","journal-title":"Brain"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Alshammri, R., Alharbi, G., Alharbi, E., and Almubark, I. (2023). Machine Learning Approaches to Identify Parkinson\u2019s Disease Using Voice Signal Features. Front. Artif. Intell., 6.","DOI":"10.3389\/frai.2023.1084001"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/S1474-4422(05)70285-4","article-title":"The Diagnosis of Parkinson\u2019s Disease","volume":"5","author":"Tolosa","year":"2006","journal-title":"Lancet Neurol."},{"key":"ref_7","unstructured":"(2025, June 25). Parkinson Disease. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/parkinson-disease."},{"key":"ref_8","unstructured":"National Institute for Health and Care Excellence (2017). Parkinson\u2019s Disease in Adults: Diagnosis and Management: Full Guideline, National Institute for Health and Care Excellence."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/s41531-025-00913-4","article-title":"Speech and Language Biomarkers for Parkinson\u2019s Disease Prediction, Early Diagnosis and Progression","volume":"11","author":"Cao","year":"2025","journal-title":"NPJ Park. Dis."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1109\/TASLP.2014.2329734","article-title":"Automatic Evaluation of Articulatory Disorders in Parkinson\u2019s Disease","volume":"22","author":"Rusz","year":"2014","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1121\/1.3514381","article-title":"Quantitative Acoustic Measurements for Characterization of Speech and Voice Disorders in Early Untreated Parkinson\u2019s Disease","volume":"129","author":"Rusz","year":"2011","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_12","unstructured":"Jaeger, H., Trivedi, D., and Stadtschnitzer, M. (2026, April 16). Mobile Device Voice Recordings at King\u2019s College London (MDVR-KCL) from Both Early and Advanced\u2019s Disease Patients and Healthy Controls 2019. Available online: https:\/\/openalex.org\/works\/W3210749297."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"10673","DOI":"10.1007\/s12652-022-04342-6","article-title":"A Generic Optimization and Learning Framework for Parkinson Disease via Speech and Handwritten Records","volume":"14","author":"Yousif","year":"2023","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Di Cesare, M.G., Perpetuini, D., Cardone, D., and Merla, A. (2024). Machine Learning-Assisted Speech Analysis for Early Detection of Parkinson\u2019s Disease: A Study on Speaker Diarization and Classification Techniques. Sensors, 24.","DOI":"10.3390\/s24051499"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1386","DOI":"10.1109\/TASLP.2023.3260709","article-title":"Exemplar-Based Sparse Representations for Detection of Parkinson\u2019s Disease from Speech","volume":"31","author":"Reddy","year":"2023","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Klemp\u00ed\u0159, O., and Krupi\u010dka, R. (2024). Analyzing Wav2Vec 1.0 Embeddings for Cross-Database Parkinson\u2019s Disease Detection and Speech Features Extraction. Sensors, 24.","DOI":"10.3390\/s24175520"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2247","DOI":"10.1007\/s13369-022-07249-8","article-title":"A Speech-Based Hybrid Decision Support System for Early Detection of Parkinson\u2019s Disease","volume":"48","author":"Lamba","year":"2023","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Huang, F., Xu, H., Shen, T., and Jin, L. (2021, January 15\u201317). Recognition of Parkinson\u2019s Disease Based on Residual Neural Network and Voice Diagnosis. Proceedings of the 2021 IEEE 5th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Xi\u2019an, China.","DOI":"10.1109\/ITNEC52019.2021.9586915"},{"key":"ref_19","unstructured":"Robert, J. (2026, April 16). Pydub PyPI 2018. GitHub. Available online: https:\/\/pypi.org\/project\/pydub\/."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bredin, H. (2023, January 20\u201324). Pyannote.Audio 2.1 Speaker Diarization Pipeline: Principle, Benchmark, and Recipe. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, Dublin, Ireland.","DOI":"10.21437\/Interspeech.2023-105"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Plaquet, A., and Bredin, H. (2023, January 20\u201324). Powerset Multi-Class Cross Entropy Loss for Neural Speaker Diarization. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, Dublin, Ireland.","DOI":"10.21437\/Interspeech.2023-205"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/09524622.2023.2259327","article-title":"Parselmouth for Bioacoustics: Automated Acoustic Analysis in Python","volume":"33","author":"Jadoul","year":"2024","journal-title":"Bioacoustics"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Alalayah, K.M., Senan, E.M., Atlam, H.F., Ahmed, I.A., and Shatnawi, H.S.A. (2023). Automatic and Early Detection of Parkinson\u2019s Disease by Analyzing Acoustic Signals Using Classification Algorithms Based on Recursive Feature Elimination Method. Diagnostics, 13.","DOI":"10.3390\/diagnostics13111924"}],"container-title":["Neurology International"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2035-8377\/18\/5\/88\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T04:14:33Z","timestamp":1779768873000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2035-8377\/18\/5\/88"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,10]]},"references-count":23,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["neurolint18050088"],"URL":"https:\/\/doi.org\/10.3390\/neurolint18050088","relation":{},"ISSN":["2035-8377"],"issn-type":[{"value":"2035-8377","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,10]]}}}