Parkinson's disease (PD) is a member of a larger group of neuromotor diseases marked by the progressive death of dopamineproducing cells in the brain. Providing computational tools for Parkinson disease using a set of data that contains medical information is very desirable for alleviating the symptoms that can help the amount of people who want to discover the risk of disease at an early stage. This paper proposes a new hybrid intelligent system for the prediction of PD progression using noise removal, clustering and prediction methods. Principal Component Analysis (PCA) and Expectation Maximization (EM) are respectively employed to address the multi-collinearity problems in the experimental datasets and clustering the data. We then apply Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) for prediction of PD progression. Experimental results on public Parkinson's datasets show that the proposed method remarkably improves the accuracy of prediction of PD progression. The hybrid intelligent system can assist medical practitioners in the healthcare practice for early detection of Parkinson disease. Parkinson's Disease (PD) is a degenerative neurological disorder marked by decreased dopamine levels in the brain [1] [2] [3] . PD is the second most common neurodegenerative disorder following Alzheimer's disease 4-8 . This disease is primarily characterized by four symptoms: tremor, rigidity, bradykinesia, and postural instability 9,10 . Though these symptoms present in varying degrees and combinations for different individuals, they are chronic and degenerative, progressively worsening over time. PD causes motor symptoms and non-motor symptoms that can affect the quality of daily life 3, 11
Furthermore, a Support Vector Regression model and Adaptive Neuro-Fuzzy Intelligent System (ANFIS) is employed to track the progression of PD and for prediction respectively REF .
Accuracy Improvement for Predicting Parkinson’s Disease Progression