Fuzzy RST and RST Rules Can Predict Effects of Different Therapies in Parkinson's Disease Patients

摘要

Neurodegenerative disorders (ND) such as Parkinson's disease (PD) are increasing in frequency with ageing, but we still do not have cure for ND. In the present study, we have analyzed results of: neurological, psychological and eye movement (saccadic) tests in order to discover patterns (KDD) and to predict disease progression with fuzzy rough set (FRST) and rough set (RST) theories. It is a longitudinal study in which we have repeated our measurements every six months and estimated disease progression in three different groups of patients: BMT-group: medication only; DBS-group medication and deep brain stimulation (DBS); and POP-group same as DBS but with several years longer period of DBS. With help of above KDD methods, we have predicted UPDRS (Unified Parkinson's Disease Rating Scale) values in the following two visits on the basis of the first visit with the accuracy of 0.7 for both BMT visits; 0.56 for DBS, and 0.7-0.8 for POP visits. We could also predict UPDRS of DBS patients by rules obtained from BMT-group with accuracy of 0.6, 0.8, and 0.7 for three following DBS visits. Using FRTS we have predicted UPDRS of DBSW3 from DBSW2 with accuracy of 0.5. We could not predict by RST disease progression of POP patients from other groups but with FRST we could predict POPW1 on the basis of DBSW1 results (with accuracy of 0.33). In summary: long-term DBS (POP-group) in contrast to other-groups has changed brain mechanisms and only FRST found similarities between POP and other-groups in disease progressions.