{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T02:41:57Z","timestamp":1787107317374,"version":"build-2736575974"},"reference-count":24,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,12]],"date-time":"2022-03-12T00:00:00Z","timestamp":1647043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Parkinson\u2019s disease (PD) is a progressive neurodegenerative disorder characterized by motor impairment, as well as tremors, stiffness, and rigidity. Besides the typical motor symptomatology, some Parkinsonians experience non-motor symptoms such as hyposmia, constipation, urinary dysfunction, orthostatic hypotension, memory loss, depression, pain, and sleep disturbances. The correct diagnosis of PD cannot be easy since there is no standard objective approach to it. After the incorporation of machine learning (ML) algorithms in medical diagnoses, the accuracy of disease predictions has improved. In this work, we have used three deep-learning-type cascaded neural network models based on the audial voice features of PD patients, called Recurrent Neural Networks (RNN), Multilayer Perception (MLP), and Long Short-Term Memory (LSTM), to estimate the accuracy of PD diagnosis. A performance comparison between the three models was performed on a sample of the subjects\u2019 voice biomarkers. Experimental outcomes suggested that the LSTM model outperforms others with 99% accuracy. This study has also presented loss function curves on the relevance of good-fitting models to the detection of neurodegenerative diseases such as PD.<\/jats:p>","DOI":"10.3390\/bioengineering9030116","type":"journal-article","created":{"date-parts":[[2022,3,13]],"date-time":"2022-03-13T22:29:43Z","timestamp":1647210583000},"page":"116","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Cascaded Deep Learning Frameworks in Contribution to the Detection of Parkinson\u2019s Disease"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0818-306X","authenticated-orcid":false,"given":"Nalini","family":"Chintalapudi","sequence":"first","affiliation":[{"name":"Centre of Clinical Research, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0603-2356","authenticated-orcid":false,"given":"Gopi","family":"Battineni","sequence":"additional","affiliation":[{"name":"Centre of Clinical Research, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2585-9260","authenticated-orcid":false,"given":"Mohmmad Amran","family":"Hossain","sequence":"additional","affiliation":[{"name":"Centre of Clinical Research, 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":"Centre of Clinical Research, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,12]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Parkinson disease-associated cognitive impairment","volume":"7","author":"Aarsland","year":"2021","journal-title":"Nat. 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