Özet:

Objective: Parkinson's disease is a chronic neurodegenerative disorder that causes movement disorder. Dopaminergic deficiency resulted from the loss of dopaminergic neurons in the substantia nigracauses the disease. UPDRS (Unified Parkinson's disease rating scale) is an important scale for evaluation of clinical severity of Parkinson's disease. Recent computational studies using in silico prediction methods show promising results in terms of their potential diagnostic relevance. This study aims to evaluate the diagnostic potential of in silico methods using vocal cord vibrations and the UPDR scale of Parkinson's Disease for obtaining more accurate diagnosis model. Material-Method: In this study an in silico prediction model using telemonitoring measures, clinical motor and total UPDRS for diagnosis of Parkinson's disease was developed by using regression analysis with neural network model. In addition, we investigated the importance of different attributes in our regression algorithm provided from telemonitoring and UPDRS for evaluation of their predictive relevance. Results: The correlation between predicted motor UPDRS score and clinical motor UPDRS score was found as 97%. Exclusion of Jitter values did not directly affect the predictive power of the model. Conclusions: Clinical UPDRS scoring proved its importance to to generate more predictive models.

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