Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27462
Title: Predicting systolic blood pressure using machine learning
Authors: Wu, TH
Pang, GKH
Kwong, EWY 
Keywords: Artificial neural network
Hypertension
Prediction
Systolic blood pressure
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: 2014 7th International Conference on Information and Automation for Sustainability: "Sharpening the Future with Sustainable Technology", ICIAfS 2014, 2014, 7069529 How to cite?
Abstract: In this paper, a new study based on machine learning technique, specifically artificial neural network, is investigated to predict the systolic blood pressure by correlated variables (BMI, age, exercise, alcohol, smoke level etc.). The raw data are split into two parts, 80% for training the machine and the remaining 20% for testing the performance. Two neural network algorithms, back-propagation neural network and radial basis function network, are used to construct and validate the prediction system. Based on a database with 498 people, the probabilities of the absolute difference between the measured and predicted value of systolic blood pressure under 10mm Hg are 51.9% for men and 52.5% for women using the back-propagation neural network With the same input variables and network status, the corresponding results based on the radial basis function network are 51.8% and 49.9% for men and women respectively. This novel method of predicting systolic blood pressure contributes to giving early warnings to young and middle-aged people who may not take regular blood pressure measurements. Also, as it is known an isolated blood pressure measurement is sometimes not very accurate due to the daily fluctuation, our predictor can provide another reference value to the medical staff. Our experimental result shows that artificial neural networks are suitable for modeling and predicting systolic blood pressure.
Description: 2014 7th International Conference on Information and Automation for Sustainability, ICIAfS 2014, Sri Lanka, 22-24 December 2014
URI: http://hdl.handle.net/10397/27462
ISBN: 9781479945986
DOI: 10.1109/ICIAFS.2014.7069529
Appears in Collections:Conference Paper

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