Rough Set Rules Determine Disease Progressions in Different Groups of Parkinson’s Patients

Abstract

Parkinson’s disease (PD) is the second after Alzheimer most popular neurodegenerative disease (ND). We do not have cure for both NDs. Therefore the purpose of our study was to predict results of different PD patients’ treatments in order to find an optimal one. We have used rough sets (RS) and machine learning (ML) rules to describe and predict disease progression (UPDRS - Unified Parkinson’s Disease Rating Scale) in three groups of Parkinson’s patients: 23 BMT patients on medication; 24 DBS patients on medication and on DBS therapy (deep brain stimulation) after surgery performed during our study; and 15 POP patients that have surgery earlier (before beginning of our study). Every PD patient had three visits approximately every 6 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, and Epworth tests we have estimated UPDRS changes (as the decision attribute). By means of ML and RS rules obtained for the first visit of BMT/DBS/POP patients we have predicted UPDRS values in next year (two visits) with the global accuracy of 70% for both BMT visits; 56% for DBS, and 67, 79% for POP second and third visits. We have used rules obtained in BMT patients to predict UPDRS of DBS patients; for first session DBSW1: global accuracy was 64%, for second DBSW2: 85% and the third DBSW3: 74% but only for DBS patients during stimulation-ON. These rules could not predict UPDRS in DBS patients during stimulation-OFF visits and in all conditions of POP patients.