AI Summary & Key Takeaways
AI InsightInaddition, we investigated the importance of different attributesin our regression algorithm provided from telemonitoring andUPDRS for evaluation of their predictive relevance.Results: The correlation between predicted motor UPDRSscore and clinical motor UPDRS score was found as 97%.Exclusion of Jitter values did not directly affect the predictivepower of the model.Conclusions: Clinical UPDRS scoring proved its importanceto achieve to generate more predictive models.
📌 3 Key Takeaways
Abstract
Objective: Parkinson’s disease is a chronicneurodegenerative impairment which causes movementimpairment. Dopaminergic deficiency resulted from the lossof dopaminergic neurons in the substantia nigracauses thedisease. UPDRS (Unified Park-inson’s disease rating scale)is an important scale for evaluation of clinical severity ofParkinson’s disease. Recent computational studies using insilico prediction methods show promising results in termsof their potential diagnostic relevance. This study aims toevaluate the diagnostic potential of in silico methods usingvocal cord vibrations and the UPDR scale of Parkinson’sDisease for obtaining more precise diagnosis model.Material-Method: In this study an in silico prediction modelusing telemonitoring measures, clinical motor and totalUPDRS for diagnosis of Parkinson’s disease was developedby using regression analysis with neural network model. Inaddition, we investigated the importance of different attributesin our regression algorithm provided from telemonitoring andUPDRS for evaluation of their predictive relevance.Results: The correlation between predicted motor UPDRSscore and clinical motor UPDRS score was found as 97%.Exclusion of Jitter values did not directly affect the predictivepower of the model.Conclusions: Clinical UPDRS scoring proved its importanceto achieve to generate more predictive models.
Özet
Amaç: Parkinson hastalığı kronik, ilerleyici ve nörolojik