{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T14:23:39Z","timestamp":1777645419007,"version":"3.51.4"},"reference-count":0,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["FI"],"published-print":{"date-parts":[[2020,12,18]]},"abstract":"<jats:p>Parkinson\u2019s disease (PD) is the second after Alzheimer\u2019s most popular neurodegenerative disease (ND). Cures for both NDs are currently unavailable. OBJECTIVE: The purpose of our study was to predict the results of different PD patients\u2019 treatments in order to find an optimal one. METHODS: We have compared rough sets (RS) and others, in short, machine learning (ML) models to describe and predict disease progression expressed as UPDRS values (Unified Parkinson\u2019s Disease Rating Scale) in three groups of Parkinson\u2019s patients: 23 BMT (Best Medical Treatment) patients on medication; 24 DBS patients on medication and on DBS therapy (Deep Brain Stimulation) after surgery performed during our study; and 15 POP (Postoperative) patients who had had surgery earlier (before the beginning of our research). Every PD patient had three visits approximately every six months. The first visit for DBS patients was before surgery. On the basis of the following condition attributes: disease duration, saccadic eye movement parameters, and neuropsychological tests: PDQ39 (Parkinson\u2019s Disease Questionnaire - disease-specific health-related quality-of-life questionnaire), and Epworth Sleepiness Scale tests we have estimated UPDRS changes (as the decision attribute). RESULTS: By means of RS rules obtained for the first visit of BMT\/DBS\/POP patients, we have predicted UPDRS values in the following year (two visits) with global accuracy of 70% for both BMT visits; 56% for DBS, and 67%, 79% for POP second and third visits. The accuracy obtained by ML models was generally in the same range, but it was calculated separately for different sessions (MedOFF\/MedON). We have used RS rules obtained in BMT patients to predict UPDRS of DBS patients; for the first session DBSW1: global accuracy was 64%, for the second DBSW2: 85% and the third DBSW3: 74% but only for DBS patients during stimulation-ON. ML models gave better accuracy for DBSW1\/W2 session S1(MedOFF): 88%, but inferior results for session S3 (MedON): 58% and 54%. Both RS and ML could not predict UPDRS in DBS patients during stimulation-OFF visits because of differences in UPDRS. By using RS rules from BMT or DBS patients we could not predict UPDRS of POP group, but with certain limitations (only for MedON), we derived such predictions for the POP group from results of DBS patients by using ML models (60%). SIGNIFICANCE: Thanks to our RS and ML methods, we were able to predict Parkinson\u2019s disease (PD) progression in dissimilar groups of patients with different treatments. It might lead, in the future, to the discovery of universal rules of PD progression and optimise the treatment.<\/jats:p>","DOI":"10.3233\/fi-2020-1969","type":"journal-article","created":{"date-parts":[[2020,12,22]],"date-time":"2020-12-22T14:57:25Z","timestamp":1608649045000},"page":"167-181","source":"Crossref","is-referenced-by-count":6,"title":["Comparison of Different Data Mining Methods to Determine Disease Progression in Dissimilar Groups of Parkinson\u2019s Patients"],"prefix":"10.1177","volume":"176","author":[{"given":"Andrzej W.","family":"Przybyszewski","sequence":"first","affiliation":[{"name":"Polish\u2013Japanese Academy of Information Technology, 00-097 Warsaw, Poland. przy@pjwstk.edu.pl"}]},{"given":"Artur","family":"Chudzik","sequence":"additional","affiliation":[{"name":"Polish\u2013Japanese Academy of Information Technology, 00-097 Warsaw, Poland. artur.chudzik@pjwstk.edu.pl"}]},{"given":"Stanislaw","family":"Szlufik","sequence":"additional","affiliation":[{"name":"Medical University of Warsaw, 03-242 Warsaw, Poland. stanislaw.szlufik@gmail.com"}]},{"given":"Piotr","family":"Habela","sequence":"additional","affiliation":[{"name":"Polish\u2013Japanese Academy of Information Technology, 00-097 Warsaw, Poland. piotr.habela@pjwstk.edu.pl"}]},{"given":"Dariusz M.","family":"Koziorowski","sequence":"additional","affiliation":[{"name":"Medical University of Warsaw, 03-242 Warsaw, Poland. dkoziorowski@esculap.pl"}]}],"member":"179","container-title":["Fundamenta Informaticae"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/FI-2020-1969","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T06:32:10Z","timestamp":1777444330000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/FI-2020-1969"}},"subtitle":[],"editor":[{"given":"Kuntal","family":"Ghosh","sequence":"additional","affiliation":[]},{"given":"Sushmita","family":"Mitra","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2020,12,18]]},"references-count":0,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/fi-2020-1969","relation":{},"ISSN":["0169-2968","1875-8681"],"issn-type":[{"value":"0169-2968","type":"print"},{"value":"1875-8681","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,18]]}}}