An Improved LightGBM Learning Method for Non-invasive Blood Pressure Prediction
To improve the accuracy of non-invasive blood pressure prediction models and reduce the impact of individual body differences on accuracy,a non-invasive blood pressure detection model WOA LightGBM based on Whale Optimization Algorithm(WOA)and Lightweight Gradient Booster(LightGBM)is proposed.The model first extracts the preprocessed features of the photocapacitive product pulse wave and electrocardiogram signal,and combines them with human body features to form an input feature matrix;Then,the input feature matrix is di-mensionally reduced using kernel principal component analysis to reduce redundancy;Finally,WOA is used to optimize the parameters ofthe LightGBM model.The experimental results show that the average absolute error of the WOA LightGBM model in predicting systolic and diastol-ic blood pressure meets the standard set by the American Association for the Advancement of Medical Devices(±5mmHg),which has certain advantages compared to traditional models and high consistency with traditional mercury meters for measuring blood pressure.
万方数据
维普
