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Early diagnosis is essential to be able to treat the symptoms. It impacts on daily activities and reduces the quality of life of both the patients and their families and it is also the second most prevalent neurodegenerative disorder after Alzheimer in people over the age of 60. Most current studies on the prediction of Parkinson\u2019s severity are carried out in advanced stages of the disease. In this work, the study analyzes a set of variables that can be easily extracted from voice analysis, making it a very non-intrusive technique. In this paper, a method based on different deep learning techniques is proposed with two purposes. On the one hand, to find out if a person has severe or non-severe Parkinson\u2019s disease, and on the other hand, to determine by means of regression techniques the degree of evolution of the disease in a given patient. The UPDRS (Unified Parkinson\u2019s Disease Rating Scale) has been used by taking into account both the motor and total labels, and the best results have been obtained using a mixed multi-layer perceptron (MLP) that classifies and regresses at the same time and the most important features of the data obtained are taken as input, using an autoencoder. A success rate of 99.15% has been achieved in the problem of predicting whether a person suffers from severe Parkinson\u2019s disease or non-severe Parkinson\u2019s disease. In the degree of disease involvement prediction problem case, a MSE (Mean Squared Error) of 0.15 has been obtained. Using a full deep learning pipeline for data preprocessing and classification has proven to be very promising in the field Parkinson\u2019s outperforming the state-of-the-art proposals.<\/jats:p>","DOI":"10.1007\/s11042-023-14932-x","type":"journal-article","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T01:02:00Z","timestamp":1685581320000},"page":"6077-6092","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Determining the severity of Parkinson\u2019s disease in patients using a multi task neural network"],"prefix":"10.1007","volume":"83","author":[{"given":"Mar\u00eda Teresa","family":"Garc\u00eda-Ord\u00e1s","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4450-349X","authenticated-orcid":false,"given":"Jos\u00e9 Alberto","family":"Ben\u00edtez-Andrades","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose","family":"Aveleira-Mata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9-Manuel","family":"Alija-P\u00e9rez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carmen","family":"Benavides","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,1]]},"reference":[{"key":"14932_CR1","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.patrec.2019.04.005","volume":"125","author":"JS Almeida","year":"2019","unstructured":"Almeida JS, Rebou\u00e7as Filho PP, Carneiro T, Wei W, Dama\u0161evi\u010dius R, Maskeli\u016bnas R et al (2019) Detecting Parkinson\u2019s disease with sustained phonation and speech signals using machine learning techniques. 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