Key result
A novel machine learning-based model using clinical and lifestyle features successfully predicted systolic blood pressure, meeting grade A BHS and AAMI standards for accuracy.
Why the study?
Traditional cuff-based BP measurement is discontinued and uncomfortable, while cuff-less ECG and PPG methods are costly and inconvenient due to multisensor collection.
Does a machine learning-based model accurately predict systolic blood pressure using clinical and lifestyle features?
Does a machine learning-based model accurately predict systolic blood pressure using clinical and lifestyle features?
A novel machine learning model utilizing basic clinical and lifestyle features can accurately estimate systolic blood pressure, meeting established clinical validation standards without the need for cuff-based or multisensor equipment.
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.
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Zheng et al. (2021) studied Blood pressure estimation. Machine learning-based systolic blood pressure predicting model was evaluated on Model accuracy for SBP estimation. A novel machine learning-based model using clinical and lifestyle features successfully predicted systolic blood pressure, meeting grade A BHS and AAMI standards for accuracy.
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