Prediction of Parkinson's Disease using Principal Component Analysis and the Markov Chains

Abstract

Parkinson's Disease (PD) is one of the neurodegenerative diseases (ND) that affects the whole life of the patients and make him/her benumb. Copious efforts have been conducted and another fraction of scientists is functional to figure out pre-diagnosis of the neurodegeneration. This proposal concomitantly tries to solve the problem for diagnosing as well as classifying the PD. With the prescribed prospect, Principal Component Analysis (PCA) is being used for extricating PD for its foremost advantage of dimension reduction with a minimal loss of information, in addition, Markov chain Model applied for classifying the PD based on the Unified Parkinson's Disease Rating Scale (UPDRS) for the reason that the chronic diseases cannot be demarcate in a deterministic pattern.