{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T16:35:29Z","timestamp":1792600529340,"version":"4.5.0"},"reference-count":24,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T00:00:00Z","timestamp":1763078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Graduate Studies and Scientific Research at Qassim University","award":["QU-APC-2025"],"award-info":[{"award-number":["QU-APC-2025"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Parkinson\u2019s disease (PD) is a degenerative neurological disorder that greatly affects motor and speech functions; therefore, early diagnosis is vital for improving patients\u2019 quality of life. This work introduces a unified and explainable AI framework for PD detection that integrates ensemble and deep learning models with transparent interpretability techniques. Methods: Acoustic features were extracted from the Parkinson\u2019s Voice Disorder Dataset, and a broad suite of machine learning and deep learning models was evaluated, including traditional classifiers (Logistic Regression, Decision Tree, KNN, Linear Regression, SVM), ensemble methods (Random Forest, Gradient Boosting, XGBoost, LightGBM), and neural architectures (CNN, LSTM, GAN). Results: The ensemble methods\u2014specifically LightGBM (LGBM) and Random Forest (RF)\u2014achieved the best performance, reaching state-of-the-art accuracy (98.01%) and ROC-AUC (0.9914). Deep learning models like CNN and GAN produced competitive results, validating their ability to capture nonlinear and generative voice patterns. XAI analysis revealed that nonlinear acoustic biomarkers such as spread2, PPE, and RPDE are the most influential predictors, consistent with clinical evidence of dysphonia in PD. Conclusions: The proposed framework achieves a strong balance between predictive accuracy and interpretability, representing a clinically relevant, scalable, and non-invasive solution for early Parkinson\u2019s detection.<\/jats:p>","DOI":"10.3390\/diagnostics15222892","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T14:37:52Z","timestamp":1763131072000},"page":"2892","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An Explainable Ensemble and Deep Learning Framework for Accurate and Interpretable Parkinson\u2019s Disease Detection from Voice Biomarkers"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4349-8710","authenticated-orcid":false,"given":"Suliman","family":"Aladhadh","sequence":"first","affiliation":[{"name":"Department of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"ref_1","first-page":"241","article-title":"Artificial intelligence revolution in healthcare: Transforming diagnosis, treatment, and patient care","volume":"7","author":"Singh","year":"2024","journal-title":"Asian J. Adv. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1111\/bcpt.14003","article-title":"A review on the new age methodologies for early detection of Alzheimer\u2019s and Parkinson\u2019s disease","volume":"134","author":"Ghosh","year":"2024","journal-title":"Basic Clin. Pharmacol. Toxicol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hossain, M.A., Traini, E., and Amenta, F. (2025). Machine Learning Applications for Diagnosing Parkinson\u2019s Disease via Speech, Language, and Voice Changes: A Systematic Review. Inventions, 10.","DOI":"10.3390\/inventions10040048"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shen, M., Mortezaagha, P., and Rahgozar, A. (2025). Explainable artificial intelligence to diagnose early Parkinson\u2019s disease via voice analysis. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-96575-6"},{"key":"ref_5","first-page":"20552076251342880","article-title":"Advanced comparative analysis of machine learning algorithms for early Parkinson\u2019s disease detection using vocal biomarkers","volume":"11","author":"Kumar","year":"2025","journal-title":"Digit. Health"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xu, H., Xie, W., Pang, M., Li, Y., Jin, L., Huang, F., and Shao, X. (2025). Non-invasive detection of Parkinson\u2019s disease based on speech analysis and interpretable machine learning. Front. Aging Neurosci., 17.","DOI":"10.3389\/fnagi.2025.1586273"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2196","DOI":"10.51519\/journalisi.v7i3.1172","article-title":"A Comparative Analysis of Machine Learning Techniques and Explainable AI on Voice Biomarkers for Effective Parkinson\u2019s Disease Prediction","volume":"7","author":"Ndlovu","year":"2025","journal-title":"J. Inf. Syst. Inform."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1007\/s41666-025-00213-9","article-title":"Through the Speech and Vocal Signals Hidden Secrets: An Explainable Methodology for Neurological Diseases Early Detection","volume":"9","author":"Vizza","year":"2025","journal-title":"J. Healthc. Inform. Res."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mukhtar, A., Khalid, S., Toor, W.T., and Akhtar, M.S. (2024, January 26\u201327). Detection of Parkinson\u2019s Disease from Voice Signals Using Explainable Artificial Intelligence. Proceedings of the 2024 3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE), Lahore, Pakistan.","DOI":"10.1109\/ETECTE63967.2024.10823755"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4013","DOI":"10.1049\/iet-ipr.2020.1048","article-title":"Machine learning and deep learning for clinical data and PET\/SPECT imaging in Parkinson\u2019s disease: A review","volume":"14","author":"Khachnaoui","year":"2020","journal-title":"IET Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"16637","DOI":"10.1007\/s12144-022-02949-8","article-title":"The performance of various machine learning methods for Parkinson\u2019s disease recognition: A systematic review","volume":"42","author":"Salari","year":"2023","journal-title":"Curr. Psychol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102909","DOI":"10.1016\/j.ipm.2022.102909","article-title":"Parkinson\u2019s disease diagnosis using neural networks: Survey and comprehensive evaluation","volume":"59","author":"Tanveer","year":"2022","journal-title":"Inf. Process. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"120541","DOI":"10.1016\/j.eswa.2023.120541","article-title":"Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson\u2019s disease: A systematic review","volume":"229","author":"Sigcha","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2399","DOI":"10.1109\/TNSRE.2023.3277749","article-title":"Multi-modal deep learning diagnosis of Parkinson\u2019s disease\u2014A systematic review","volume":"31","author":"Skaramagkas","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"119651","DOI":"10.1016\/j.eswa.2023.119651","article-title":"Machine learning-and statistical-based voice analysis of Parkinson\u2019s disease patients: A survey","volume":"219","author":"Amato","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"45205","DOI":"10.1007\/s11042-023-15414-w","article-title":"Comparative analysis of machine learning techniques for Parkinson\u2019s detection: A review","volume":"82","author":"Khanna","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"69393","DOI":"10.1007\/s11042-024-18186-z","article-title":"A comparative study: Prediction of parkinson\u2019s disease using machine learning, deep learning and nature inspired algorithm","volume":"83","author":"Keserwani","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e25469","DOI":"10.1016\/j.heliyon.2024.e25469","article-title":"A review of machine learning and deep learning algorithms for Parkinson\u2019s disease detection using handwriting and voice datasets","volume":"10","author":"Islam","year":"2024","journal-title":"Heliyon"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"72997","DOI":"10.1007\/s11042-024-18398-3","article-title":"Machine learning and deep learning approach to Parkinson\u2019s disease detection: Present state-of-the-art and a bibliometric review","volume":"83","author":"Sabherwal","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Giannakopoulou, K.-M., Roussaki, I., and Demestichas, K. (2022). Internet of things technologies and machine learning methods for Parkinson\u2019s disease diagnosis, monitoring and management: A systematic review. Sensors, 22.","DOI":"10.3390\/s22051799"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rana, A., Dumka, A., Singh, R., Panda, M.K., Priyadarshi, N., and Twala, B. (2022). Imperative role of machine learning algorithm for detection of Parkinson\u2019s disease: Review, challenges and recommendations. Diagnostics, 12.","DOI":"10.3390\/diagnostics12082003"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"32215","DOI":"10.1007\/s11042-022-12960-7","article-title":"Machine learning Ensemble for the Parkinson\u2019s disease using protein sequences","volume":"81","author":"Arora","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1038\/s41746-022-00568-y","article-title":"Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson\u2019s disease motor symptoms","volume":"5","author":"Chandrabhatla","year":"2022","journal-title":"NPJ Digit. Med."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Little, M., McSharry, P., Hunter, E., Spielman, J., and Ramig, L. (2009). Suitability of dysphonia measurements for telemonitoring of Parkinson\u2019s disease. IEEE Trans. Biomed. Eng., 56.","DOI":"10.1038\/npre.2008.2298.1"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/22\/2892\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T15:51:29Z","timestamp":1763394689000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/22\/2892"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,14]]},"references-count":24,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["diagnostics15222892"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15222892","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,14]]}}}