{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T04:48:28Z","timestamp":1784609308328,"version":"3.55.0"},"reference-count":12,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2016,9,14]],"date-time":"2016-09-14T00:00:00Z","timestamp":1473811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Polish National Science Centre","award":["Dec-2011\/03\/B\/ST6\/03816"],"award-info":[{"award-number":["Dec-2011\/03\/B\/ST6\/03816"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We still do not know how the brain and its computations are affected by nerve cell deaths and their compensatory learning processes, as these develop in neurodegenerative diseases (ND). Compensatory learning processes are ND symptoms usually observed at a point when the disease has already affected large parts of the brain. We can register symptoms of ND such as motor and\/or mental disorders (dementias) and even provide symptomatic relief, though the structural effects of these are in most cases not yet understood. It is very important to obtain early diagnosis, which can provide several years in which we can monitor and partly compensate for the disease\u2019s symptoms, with the help of various therapies. In the case of Parkinson\u2019s disease (PD), in addition to classical neurological tests, measurements of eye movements are diagnostic. We have performed measurements of latency, amplitude, and duration in reflexive saccades (RS) of PD patients. We have compared the results of our measurement-based diagnoses with standard neurological ones. The purpose of our work was to classify how condition attributes predict the neurologist\u2019s diagnosis. For n = 10 patients, the patient age and parameters based on RS gave a global accuracy in predictions of neurological symptoms in individual patients of about 80%. Further, by adding three attributes partly related to patient \u2018well-being\u2019 scores, our prediction accuracies increased to 90%. Our predictive algorithms use rough set theory, which we have compared with other classifiers such as Na\u00efve Bayes, Decision Trees\/Tables, and Random Forests (implemented in KNIME\/WEKA). We have demonstrated that RS are powerful biomarkers for assessment of symptom progression in PD.<\/jats:p>","DOI":"10.3390\/s16091498","type":"journal-article","created":{"date-parts":[[2016,9,14]],"date-time":"2016-09-14T10:45:00Z","timestamp":1473849900000},"page":"1498","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson\u2019s Patients"],"prefix":"10.3390","volume":"16","author":[{"given":"Andrzej","family":"Przybyszewski","sequence":"first","affiliation":[{"name":"Polish-Japanese Academy of Information Technology, 02-008 Warszawa, Poland"},{"name":"Department Neurology, University of Massachusetts Medical School, Worcester, MA 01655, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Kon","sequence":"additional","affiliation":[{"name":"Mathematics and Statistics, Boston University, Boston, MA 02215, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stanislaw","family":"Szlufik","sequence":"additional","affiliation":[{"name":"Neurology, Faculty of Health Science, Medical University of Warsaw, Warszawa 03-242, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Artur","family":"Szymanski","sequence":"additional","affiliation":[{"name":"Polish-Japanese Academy of Information Technology, 02-008 Warszawa, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Piotr","family":"Habela","sequence":"additional","affiliation":[{"name":"Polish-Japanese Academy of Information Technology, 02-008 Warszawa, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dariusz","family":"Koziorowski","sequence":"additional","affiliation":[{"name":"Neurology, Faculty of Health Science, Medical University of Warsaw, Warszawa 03-242, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2016,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gelbukh, A., Espinoza, F.C., and Galicia-Haro, S.N. (2014). Nature-Inspired Computation and Machine Learning, Springer International Publishing.","DOI":"10.1007\/978-3-319-13650-9"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1017\/S0952523800174012","article-title":"Striate cortex increases contrast gain of macaque LGN neurons","volume":"17","author":"Przybyszewski","year":"2000","journal-title":"Vis. Neurosci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Peters, J.F., Skowron, A., and Rybi\u0144ski, H. (2008). Transactions on Rough Sets IX, Springer-Verlag.","DOI":"10.1007\/978-3-540-89876-4"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.cogsys.2008.08.006","article-title":"Logical Rules of Visual Brain: From Anatomy through Neurophysiology to Cognition","volume":"11","author":"Przybyszewski","year":"2010","journal-title":"Cogn. Syst. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2","DOI":"10.3389\/fnint.2012.00002","article-title":"Deep Brain Stimulation for Movement Disorders","volume":"6","author":"Pizzolato","year":"2012","journal-title":"Front. Integr. Neurosci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pawlak, Z. (1991). Rough Sets, Springer Netherlands.","DOI":"10.1007\/978-94-011-3534-4"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Or\u0142owska, P.D.E. (1998). Incomplete Information: Rough Set Analysis, Physica-Verlag HD.","DOI":"10.1007\/978-3-7908-1888-8"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ziarko, W., and Yao, Y. (2001). Rough Sets and Current Trends in Computing, Springer.","DOI":"10.1007\/3-540-45554-X"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lavrac, N., and Wrobel, S. (1995). Machine Learning: ECML-95, Springer.","DOI":"10.1007\/3-540-59286-5"},{"key":"ref_11","unstructured":"John, G., and Langley, P. (,  1995). Estimating Continuous Distributions in Bayesian Classifiers. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, San Francisco, CA, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Polkowski, P.L., Tsumoto, P.S., and Lin, P.T.Y. (2000). Rough Set Methods and Applications, Physica-Verlag HD.","DOI":"10.1007\/978-3-7908-1840-6"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/9\/1498\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:30:57Z","timestamp":1760211057000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/9\/1498"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,9,14]]},"references-count":12,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2016,9]]}},"alternative-id":["s16091498"],"URL":"https:\/\/doi.org\/10.3390\/s16091498","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,9,14]]}}}