Performance evaluation of Random Forest regression model in tracking Parkinson's disease progress

In this paper, capabilities of the Random Forest algorithm are tested with application to the Parkinson's disease progression that can be determined from speech. Results are compared with the linear regression model and the Classification and Regression Tree method. Mean Squared Error and Mean Absolute Error values were calculated and compared for each of the approaches. The Random Forest algorithm belongs to the group model category and usually improves the results achieved by regression trees, making it more suitable for fighting the disease.