Classification of hypertension in pregnancy based on random forest and Xgboost fusion model

Conference: BIBE 2019 - The Third International Conference on Biological Information and Biomedical Engineering
06/20/2019 - 06/22/2019 at Hangzhou, China

Proceedings: BIBE 2019

Pages: 5Language: englishTyp: PDF

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Lan, Xinke; Wang, Jun (Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing, China)
Wu, Wei (Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China)
Peng, Danhong; Xu, Tian; Wang, Gongdao (Department of Obstetrics and Gynecology, Zhongda Hospital, Southeast University, Nanjing, China)
Hou, Fengzhen (School of Science,China Pharmaceutical University, Nanjing, China)

Hypertension and its complications during pregnancy are the second most important factors affecting maternal mortality, posing a serious threat to pregnant women and newborns. The pathogenesis and influencing factors of clinical hypertension during pregnancy are still unclear. In this context, this paper proposes a classification model of pregnancy-induced hypertension based on random forest and Xgboost algorithm, and uses three methods to classify pregnancy-induced hypertension and analyzes the importance of related features. The experimental results show that the classification accuracy of the fusion model is about 83.68%, and the auc value is 0.88, which is more accurate and better than the single random forest and Xgboost model. The experimental results show that the characteristic scores of blood pressure and patient height and body mass index are higher than those of calcium and other features, and play a greater role in model classification.