A Novel Machine Learning-Based Systolic Blood Pressure Predicting Model

Blood pressure (BP) is a vital biomedical feature for diagnosing hypertension and cardiovascular diseases. Traditionally, it is measured by cuff-based equipment, e.g., sphygmomanometer; the measurement is discontinued and uncomfortable. A cuff-less method based on different signals, electrocardiogram (ECG) and photoplethysmography (PPG), is proposed recently. However, this method is costly and inconvenient due to the collections of multisensors. In this paper, a novel machine learning-based systolic blood pressure (SBP) predicting model is proposed. The model was evaluated by clinical and lifestyle features (gender, marital status, smoking status, age, weight, etc.). Different machine learning algorithms and different percentage of training, validation, and testing were evaluated to optimize the model accuracy. Results were validated to increase the accuracy and robustness of the model. The performance of our model met both the level of grade A (British Hypertension Society (BHS) standard) and the American National Standard from the Association for the Advancement of Medical Instrumentation (AAMI) for SBP estimation.

Automated summary

This paper proposes a novel machine learning-based systolic blood pressure (SBP) predicting model that is evaluated using clinical and lifestyle features, utilizing different algorithms and training/validation/testing proportions to optimize accuracy. Results validate the model's performance meeting the standards of both the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI).