ITM Web Conf.
Volume 11, 20172017 International Conference on Information Science and Technology (IST 2017)
|Number of page(s)
|Session I: Computational Intelligence
|23 May 2017
Prediction of Postpartum Hemorrhage Volume of Pregnant Women Based on GA-SVM Algorithm
1 Nanjing Tech University, University, 211816, China
2 Nanjing Health Information Center, 210003 China
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.
© Owned by the authors, published by EDP Sciences, 2017
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