Machine Learning Prediction of Heart Health Status from Mobile PPG Sensor Data

We consider the use of machine learning for convenient, inexpensive monitoring of heart health status using a wrist-worn photoplethysmography (PPG) sensor. Patients' health status is assessed via a standard questionnaire-based measure of symptom severity previously shown to provide information relevant to survival. In a pilot study yielding a nearly classbalanced collection of 198 data records from 15 participants who have been diagnosed with heart failure (heart insufficiency), we attain a macro-averaged classification accuracy of 0.73 using a random forest classifier to predict binarized symptom severity from heart rate variability (HRV) and physical activity metrics extracted from the PPG signals. Our results support the possibility of reducing hospital readmission rates through inhome monitoring, while limiting the frequency of inconvenient and expensive in-person assessments in a clinical setting.