Abstract: Aiming at the problems of nonstandard blood pressure measurement, large fluctuation range and low prediction accuracy, a support vector regression (SVR) blood pressure prediction algorithm based on grid search with cross validation is proposed. The algorithm first cleans the data and then finds the optimal parameter pair by combining grid search and cross validation. Then, it establishes the corresponding blood pressure prediction model by analyzing the implicit relationship between heart rate, blood oxygen and blood pressure of human physiological index data. Finally, the predicted results are compared with other several kinds of blood pressure prediction models. The results of classical machine learning model are compared, and the accuracy and rootmean square error are used to evaluate. The experimental results show that the prediction accuracy of this algorithm is about 71.39% and 81.69% respectively for high and low pressures, and the root mean square error is about 0.5349 and 0.4279, which are obviously superior to the traditional machine learning algorithms.

Key words: physiological index data, grid search, cross validation, blood pressure prediction, SVR