Key Marker Selection for the Detection of Early Parkinson' s Disease using Importance-Driven Models

The detection of early Parkinson' s disease (PD) is crucial for PD management. Most of previous efforts on PD diagnosis focus more on improving PD detection accuracies by trying using features from more modalities, which results in a common question: is it true that the more features available, the better the performance of the diagnosis system? This paper proposes an importance-driven approach for the detection of PD. The importance of features based on gradient boosting is firstly learned. The ranked features based on feature importance are input to a progressive learning pipeline to find key features of PD. The experiment results show that a comparable PD classification performance can be obtained with much less key features and therefore fewer modalities of tests are required. Such findings have critical socioeconomic values.