Multimodal Learning Determines Rules of Disease Development in Longitudinal Course with Parkinson’s Patients
Parkinson’s disease (PD) is neurodegenerative disease (ND) related to the lost of dopaminergic neurons that elevates first by motor and later also by non-motor (dementia, depression) disabilities. Actually, there is no cure for ND as we are not able to revive death cells. Our purpose was to find, with help of data mining and machine learning (ML), rules that describe and predict disease progression in two groups of PD patients: 23 BMT patients that are taking only medication; 24 DBS patients that are on medication and on DBS (deep brain stimulation) therapies. In the longitudinal course of PD there were three visits approximately every 6 months with the first visit for DBS patients before electrode implantation. We have estimated disease progression as UPDRS (unified Parkinson’s disease rating scale) changes on the basis of patient’s disease duration, saccadic eye movement parameters, and neuropsychological tests: PDQ39, and Epworth tests. By means of ML and rough set theory we found rules on the basis of the first visit of BMT patients and used them to predict UPDRS changes in next two visits (global accuracy was 70% for both visits). The same rules were used to predict UPDRS in the first visit of DBS patients (global accuracy 71%) and the second (78%) and third (74%) visit of DBS patients during stimulation-ON. These rules could not predict UPDRS in DBS patients during stimulation-OFF visits. In summary, relationships between condition and decision attributes were changed as result of the surgery but restored by electric brain stimulation.
KeywordsNeurodegenerative disease Rough set Decision rules Granularity
This work was partly supported by projects Dec-2011/03/B/ST6/03816, from the Polish National Science Centre.
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