Parkinson's Disease Prediction Using Graphic User Interface-Machine Learning Algorithm

Abstract

Machine learning techniques and computational methods are used for predicting Parkinson disease from Electroencephalograms (EEG) signals. In this investigation, with primarily studies analysis of the performance metric characteristics we have developed a graphic user interface application to perform the diagnostic of pathology on Parkinson’s disease (PD) based on the K-Nearest Neighbours classifier. The system is implemented and simulated in Anaconda, and its performance is tested on real dataset that contains 22 features, with the total of 195 instances and two (02) classes. Each class, an abnormal class (patients having PD) and a normal class (healthy patients) consists of 48 instances, and 147 instances, respectively. The simulation results achieved better training accuracy, slightly higher than the test accuracy in the range of [75.7%~100%] and [75.0%~92.3%], respectively. The prediction performance of the class status maybe optimized by combining the dataset set size with the k-neighbors parameters. It can be improved by adjusting k and the size in the range of [2~9], and [15%~35%], which results in increasing the interval of the accuracy in the range of [86.7~100] % for the training set and [86.4~91,8] % for the test set. The proposed method shows the effectiveness of identifying a normal and an abnormal class status from the statistical proprieties of the Electroencephalograms (EEG) signals. For quality analysis, the present technique can serve as a test platform for measurement and verification of the pathology disease. it can be used as a guideline to examine the performance metrics that tells us how much better the proposed model is than making prediction. The results maybe useful to design a better GUI, maximizing the accuracy prediction helping the medical doctors to diagnostic a patient effectively in a reduced time lapse and taking a rapid decision.