A Novel Machine Learning-Based Systolic Blood Pressure Predicting Model

Zheng, J; Yu, Z

A Novel Machine Learning-Based Systolic Blood Pressure Predicting Model

Zheng, J Yu, Z

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Publisher:: Hindawi Publishing Corporation
Publication Type:: Journal Article
Citation:: Journal of Nanomaterials, 2021, 2021, pp. 1-8
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Zheng, J
dc.contributor.author	Yu, Z https://orcid.org/0000-0002-8888-4570
dc.date.accessioned	2022-10-23T23:46:06Z
dc.date.available	2022-10-23T23:46:06Z
dc.date.issued	2021-01-01
dc.identifier.citation	Journal of Nanomaterials, 2021, 2021, pp. 1-8
dc.identifier.issn	1687-4110
dc.identifier.issn	1687-4129
dc.identifier.uri	http://hdl.handle.net/10453/162606
dc.description.abstract	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.
dc.language	English
dc.publisher	Hindawi Publishing Corporation
dc.relation.ispartof	Journal of Nanomaterials
dc.relation.isbasedon	10.1155/2021/9934998
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0912 Materials Engineering, 1007 Nanotechnology
dc.subject.classification	Nanoscience & Nanotechnology
dc.title	A Novel Machine Learning-Based Systolic Blood Pressure Predicting Model
dc.type	Journal Article
utslib.citation.volume	2021
utslib.for	0912 Materials Engineering
utslib.for	1007 Nanotechnology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Corporate Services)
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-10-23T23:46:04Z
pubs.publication-status	Published
pubs.volume	2021

Abstract:

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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http://hdl.handle.net/10453/162606