Parkinson Hastalığının Teşhisinde YSA Destekli Karar Sistemi Başarımı

Abstract

Parkinson's disease (PD) is a neurodegenerative disease that results in the loss of function of dopamine-producing brain cells. Primer designation of PD; is seen as tremor in the upper and lower limbs in 70% of the patients, and as in slowing and stiffness in the movement in 30% of them. Archimedes spiral technique is a clinical test method developed for examining PD motor disorders. The reliability and validity of the spiral test drawing technique was statistically proven by comparing it with the Unified Predictive Rating Scale (UPDRS). In this study, it was aimed to construct a static spiral test and a dynamic spiral test drawings, to extract the characteristics using the signal processing techniques and to identify the Parkinson's disease using the artificial neural network model. In the classification of the disease, only SST and ANN using only DST and f score ratio in the classification were found to be 0.95 and 0.92, respectively. When SST and DST methods were evaluated together, ANN classification success was found to be 0.99. For this reason, it was found that SST and DST methods were more successful in the classification of the disease than the classification using SST and DST alone. Using the combination of SST and DST data as a result of the study, PD was classified with artificial intelligence techniques with an accuracy of 98.6% and a score of 0.99 f.