Predicting hypertension using PPG sensor data and demographic factors: A machine learning approach

Background Hypertension is one of the most important health-related problems worldwide, and its monitoring is necessary constantly. Objective The regular methods of blood pressure monitoring have disadvantages; hence, the interest in finding better solutions is stirred. Methods In this study, PPG signals from 218 subjects in Guilin People's Hospital were analyzed, where 657 PPG recordings were employed together with demographic and clinical data. CNN-Attention, CNN-GRU, and LSTM, have been conducted with z-score normalization and augmentation in an 80:20 train-test split. Results The highest performance of the CNN-GRU model achieved 75% accuracy, an AUC-ROC of 0.658, and perfect recall for hypertensive cases at 1.00. While the CNN-Attention model reached an accuracy of 61%, the overall poorest performance was given by LSTM. Conclusion These results prove that accessible cardiovascular monitoring is feasible and valuable in a resource-limited settings.