PDGes: An Interpretable Detection Model for Parkinson's Disease Using Smartphones

Parkinson's disease (PD) is a neurodegenerative disorder that severely affects the motor system of patients. Early PD detection will greatly improve the quality of lives. However, existing automatic PD detection systems either rely on customized sensors or require users to perform special activities, using machine learning models whose prediction process is not understandable by medical professionals. In this article, we develop a non-disruptive PD detection system on smartphones based on interpretable prediction models. We design an application named PDGes to passively collect touchscreen and Inertial Measurement Unit data of users' tapping and swiping actions on smartphones. Meaningful features that reflect finger dexterity, tremor, stiffness, and hand movement are extracted to build the prediction model. To better comprehend the decisions made by the model, we conduct a systematic analysis of feature importance to help validate the conformity of the model with clinical PD diagnosis. We collected data from 108 volunteers to evaluate the performance of PDGes. The experiment results show that PDGes achieves a detection accuracy of more than 94.5% on different smartphones.