One of the most widely spread diseases around the world is Parkinson's disease (PD). This disease affects the human brain and results in sudden and random body movements. It progresses slowly and differently at every stage. Moreover, the disease has few known symptoms. Therefore, it is difficult for doctors to discover it in its initial stages. One of the main symptoms that can help researchers to predict the disease as early as possible is speech disorder. Many researchers have conducted several studies using voice recordings to produce an accurate PD diagnosis system. One unique promising way to use the speech disorder as a helping factor to predict PD is by using machine learning techniques. In this paper, we used NNge classification algorithms to analyze voice recordings for PD classification. NNge classification is known to be an efficient algorithm for analyzing voice signals but has not been explored in details in this area. In this paper, a literature review of previous research papers about PD prediction was briefly presented. Then, an experiment using NNge classification algorithm to classify people into healthy people and PD patients was performed. The parameters of the NNge algorithm were optimized. Moreover, SMOTE algorithm was used to balance the data. Finally, NNge and ensemble algorithms specifically, AdaBoostM1 was implemented on the balanced data. The final implementation of NNge using AdaBoost ensemble classifier had an accuracy of 96.30%.
