Bio-medical application on predicting systolic blood pressure using neural networks

Abstract

This paper presents a new study based on artificial neural network, which is a typical technique for processing big data, for the prediction of systolic blood pressure by correlated factors (gender, serum cholesterol, fasting blood sugar and electrocardiography signal). Two neural network algorithms, back-propagation neural network and radial basis function network, are used to construct and validate the bio-medical prediction system. The database of raw data is divided into two parts: 80% for training the neural network and the remaining 20% for testing the performance. The experimental result shows that artificial neural networks are suitable for modeling and predicting systolic blood pressure. This novel method of predicting systolic blood pressure contributes to giving early warnings to adults who may not take regular blood pressure measurements. Also, as it is known that 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.