Identification of the stages of Parkinson’s Disease using Machine Learning Techniques
Parkinson’s Disease (PD) is a degenerative disease of the central nervous system. This work performs a four-class classification using the motor assessments of subjects obtained from the Parkinson’s Progressive Markers Initiative (PPMI) database and a variety of techniques like Deep Neural Network (DNN), Support Vector Machine (SVM), Deep Belief Network (DBN) etc. The effect of using feature selection was also studied. Due to the skewness of the data, while evaluating the performance of the classifier, along with accuracy other metrics like precision, recall and F1-score were also used. The best classification performance was obtained when a feature selection technique based on Joint Mutual Information (JMI) was used for selecting the features that were then used as input to the classification algorithm like SVM. Such a combination of SVM and feature selection algorithm based on JMI yielded an average classification accuracy of 87.34 % and an F1-score of 0.84.
