Prediction of Parkinson's Disease using Hybrid Feature Selection based Techniques

Parkinson's disease (PD) is one of the significant severe problems globally in recent times. It is a neurological disorder that progresses over time and the most severe problems after Alzheimer's disease. Our article proposes a Hybrid Feature Selection system for the initial detection of PD from speech recordings. This method picks the best set of instances that can lessen instance vector dimensions from 22 to 5. We have proposed a machine learning-based model using five different classifiers named Random Forest, Logistic Regression, XGBoost, AdaBoost, and Gradient Tree Boosting. Gradient Tree Boosting presents the best appearance with a spectacular accuracy of 98.31% and the area under the ROC curve 98.66%, among all classifiers used to predict PD. We showed that the stated design has greater accuracy than the current methods available in the literature, and the number of instances is less than others.