An optimal dimension reduction strategy and experimental evaluation for Parkinson's disease classification

The amount of data streamed and generated through various healthcare systems is exponentially increasing day by day. Applying traditional data mining algorithms on this massive sized data to construct automated decision support systems is a tedious and time consuming task. In recent years, there has been increasing interest in the development of telediagnosis and telemonitoring systems for Parkinson's disease (PD). Parkinson's disease is a progressive neurodegenerative disease which affect the movement characteristics. PD patients commonly face vocal impairments during the early stages of the disease. This work proposes a computationally efficient method for dimension reduction and classification of healthcare related data. The devised framework is capable to deal with the data having discrete as well as continuous natured features. The experimental evaluation is performed on Parkinson's disease classification database (Sakar et al., 2018). The statistical performance metrices used are - validation and test accuracy, precision, recall, F1-score, etc. There will be computational complexity advantages when this reduced dimension data is further processed for modelling and building prediction system. In order to prove the optimality of proposed framework, comparative analysis is performed with the significant existing approaches.