Mining Discriminative Patterns to Predict Health Status for Cardiopulmonary Patients

Smartphones are ubiquitous now, but it is still unclear what physiological functions they can monitor at clinical quality. Pulmonary function is a standard measure of health status for cardiopulmonary patients. We have shown that predictive models can accurately classify cardiopulmonary conditions from healthy status, as well as different severity levels within cardiopulmonary disease, the GOLD stages. Here we propose several universal models to monitor cardiopulmonary conditions, including DPClass, a novel learning approach we designed. We carefully prepare motion dataset covering status from GOLD 0 (healthy), GOLD 1 (mild), GOLD 2 (moderate), all the way to GOLD 3 (severe). Sixty-six subjects participate in this study. After de-identification, their walking data are applied to train the predictive models. The RBF-SVM model yields the highest accuracy while the DPClass model provides better interpretation of the model mechanisms. We not only provide promising solutions to monitor health status by simply carrying a smartphone, but also demonstrate how demographics influences predictive models of cardiopulmonary disease.