<![CDATA[Parkinson's disease is one of the neurodegenerative disorders that arises due to various risk factors, such as age, gender, and other contributing factors. Therefore, early detection of Parkinson's disease is crucial to prevent the condition from worsening. To develop an automated detection system for Parkinson's disease, a medical record dataset is required, consisting of frequency and amplitude data from the voice waves of several subjects. One of the main challenges in detecting Parkinson's disease is effectively analyzing this data. Additionally, a system that can quickly and automatically analyze clinical data is necessary. In response to this need, we propose the development of an automated system using the decision tree method to detect Parkinson's disease. This method can improve the system's performance in diagnosing whether an individual is affected by Parkinson's disease or not. The results of our proposed method show an accuracy of 90%, which is superior by 8%, 10%, 14.5%, and 20% compared to Naïve Bayes, SVM, K- NN, and other Decision Tree methods.]]>
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