{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T17:03:03Z","timestamp":1778173383185,"version":"3.51.4"},"reference-count":93,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,18]],"date-time":"2023-02-18T00:00:00Z","timestamp":1676678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Parkinson\u2019s Disease (PD) is one of the most common non-curable neurodegenerative diseases. Diagnosis is achieved clinically on the basis of different symptoms with considerable delays from the onset of neurodegenerative processes in the central nervous system. In this study, we investigated early and full-blown PD patients based on the analysis of their voice characteristics with the aid of the most commonly employed machine learning (ML) techniques. A custom dataset was made with hi-fi quality recordings of vocal tasks gathered from Italian healthy control subjects and PD patients, divided into early diagnosed, off-medication patients on the one hand, and mid-advanced patients treated with L-Dopa on the other. Following the current state-of-the-art, several ML pipelines were compared usingdifferent feature selection and classification algorithms, and deep learning was also explored with a custom CNN architecture. Results show how feature-based ML and deep learning achieve comparable results in terms of classification, with KNN, SVM and na\u00efve Bayes classifiers performing similarly, with a slight edge for KNN. Much more evident is the predominance of CFS as the best feature selector. The selected features act as relevant vocal biomarkers capable of differentiating healthy subjects, early untreated PD patients and mid-advanced L-Dopa treated patients.<\/jats:p>","DOI":"10.3390\/s23042293","type":"journal-article","created":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T02:29:08Z","timestamp":1676860148000},"page":"2293","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":77,"title":["Artificial Intelligence-Based Voice Assessment of Patients with Parkinson\u2019s Disease Off and On Treatment: Machine vs. Deep-Learning Comparison"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8675-5532","authenticated-orcid":false,"given":"Giovanni","family":"Costantini","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, Italy"}]},{"given":"Valerio","family":"Cesarini","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0253-559X","authenticated-orcid":false,"given":"Pietro","family":"Di Leo","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, Italy"}]},{"given":"Federica","family":"Amato","sequence":"additional","affiliation":[{"name":"Department of Control and Computer Engineering, Polytechnic University of Turin, 10129 Turin, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9903-5550","authenticated-orcid":false,"given":"Antonio","family":"Suppa","sequence":"additional","affiliation":[{"name":"Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy"},{"name":"IRCCS Neuromed Institute, 86077 Pozzilli, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0717-3521","authenticated-orcid":false,"given":"Francesco","family":"Asci","sequence":"additional","affiliation":[{"name":"Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy"},{"name":"IRCCS Neuromed Institute, 86077 Pozzilli, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8432-594X","authenticated-orcid":false,"given":"Antonio","family":"Pisani","sequence":"additional","affiliation":[{"name":"Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy"},{"name":"IRCCS Mondino Foundation, 27100 Pavia, Italy"}]},{"given":"Alessandra","family":"Calculli","sequence":"additional","affiliation":[{"name":"Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy"},{"name":"IRCCS Mondino Foundation, 27100 Pavia, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9034-9921","authenticated-orcid":false,"given":"Giovanni","family":"Saggio","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-018-0305-z","article-title":"Artificial intelligence in healthcare","volume":"2","author":"Yu","year":"2018","journal-title":"Nat. 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