Improved KNN algorithm with information entropy for the diagnosis of Parkinson's disease
Zhaozhao Fang
Abstract
Parkinson’s Disease (PD) has become the second most common degenerative disease for the elderly. The most important feature of PD is the loss of neuronal function, which in turn greatly affects the motor function of the individual. At present, the early diagnosis of PD relies mainly on clinical symptoms, which is largely dependent on the experience of clinicians. In order to establish an auxiliary diagnosis system for PD, this paper mainly introduces machine learning methods, specifically, the KNN algorithm, Random Forest algorithm, and Naive Bayesian algorithm are utilized to conduct group decision for PD, where, an improved KNN algorithm with information entropy is proposed. The experiments on actual clinical data are designed and the comparison results show that this method does improve the prediction accuracy effectively and proves the feasibility of this method.