Prediction of Postpartum Hemorrhage Volume of Pregnant Women Based on GA-SVM Algorithm

Abstract

To take most advantage of the medical data resources from maternal and child health information platform and to improve the medical level, the team bring up a method based on support vector machine (SVM) algorithm which is aimed at predicting blood flow and blood pressure within 2-24 hours after parturition. We cleaned up the extracted data, determine the linear correlation via Pearson correlation coefficient, and utilize the significance to test and justify the relevance of data. Also, genetic algorithm is used to optimize the parameters. Then, we filter out the data with strong correlation coefficient and make predictions through the SVM algorithm. Finally, we determine the effectiveness of the prediction by doing the comparison between predicted results and the real data. The experiments show that, SVM is valid and feasible for the prediction of postpartum hemorrhage and the blood pressure.