{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:33:49Z","timestamp":1776890029722,"version":"3.51.2"},"reference-count":36,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,5,27]],"date-time":"2020-05-27T00:00:00Z","timestamp":1590537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["TIN2017-88728-C2-1-R"],"award-info":[{"award-number":["TIN2017-88728-C2-1-R"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009473","name":"Universidad de M\u00e1laga","doi-asserted-by":"publisher","award":["Project DIATAX"],"award-info":[{"award-number":["Project DIATAX"]}],"id":[{"id":"10.13039\/100009473","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Application of deep learning (DL) to the field of healthcare is aiding clinicians to make an accurate diagnosis. DL provides reliable results for image processing and sensor interpretation problems most of the time. However, model uncertainty should also be thoroughly quantified. This paper therefore addresses the employment of Monte Carlo dropout within the DL structure to automatically discriminate presymptomatic signs of spinocerebellar ataxia type 2 in saccadic samples obtained from electrooculograms. The current work goes beyond the common incorporation of this special type of dropout into deep neural networks and uses the uncertainty derived from the validation samples to construct a decision tree at the register level of the patients. The decision tree built from the uncertainty estimates obtained a classification accuracy of 81.18% in automatically discriminating control, presymptomatic and sick classes. This paper proposes a novel method to address both uncertainty quantification and explainability to develop reliable healthcare support systems.<\/jats:p>","DOI":"10.3390\/s20113032","type":"journal-article","created":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T12:36:58Z","timestamp":1590669418000},"page":"3032","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals"],"prefix":"10.3390","volume":"20","author":[{"given":"Catalin","family":"Stoean","sequence":"first","affiliation":[{"name":"Romanian Institute of Science and Technology, 400022 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruxandra","family":"Stoean","sequence":"additional","affiliation":[{"name":"Romanian Institute of Science and Technology, 400022 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5158-5905","authenticated-orcid":false,"given":"Miguel","family":"Atencia","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics, Universidad de M\u00e1laga, 29071 M\u00e1laga, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moloud","family":"Abdar","sequence":"additional","affiliation":[{"name":"Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong 3216, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luis","family":"Vel\u00e1zquez-P\u00e9rez","sequence":"additional","affiliation":[{"name":"Cuban Academy of Sciences, La Habana 10100, Cuba"},{"name":"Center for Research and Rehabilitation of Hereditary Ataxias, Holgu\u00edn 80100, Cuba"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abbas","family":"Khosravi","sequence":"additional","affiliation":[{"name":"Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong 3216, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0360-5270","authenticated-orcid":false,"given":"Saeid","family":"Nahavandi","sequence":"additional","affiliation":[{"name":"Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong 3216, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2689-8552","authenticated-orcid":false,"given":"U. 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