Intelligent Evaluation of Fetal Health Status Based on Stacking Ensemble Model
HAO Jingyu1, CHEN Yi2, WU Shuicai1
1. Faculty of Environmental and Life, Beijing University of Technology, Beijing 100124, China; 2. Department of Obstetrics and
Gynecology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing 100124, China
Abstract:Objective To study a machine learning algorithm for evaluating fetal state in utero during pregnancy, and to propose
a new intelligent evaluation method for fetal state in utero based on Stacking ensemble model. Methods In the feature selection
stage, XGBoost and HeatMap were used to analyze the public fetal heart data set, and the optimal feature subset was selected. At the
classification stage, the fetus was evaluated using a new method fused with a two-layer Stacking model, the first layer was trained
with five strong machine learning models, and the second layer was trained with a Logistics regression model. Results Using the
fetal heart data test set, ACC and AUC were 0.950 and 0.980 respectively. Conclusion A new method based on Stacking ensemble
model can assist clinicians in the diagnosis of fetal intrauterine health status.
郝婧宇1,陈奕2,吴水才1. 基于Stacking模型融合的胎儿健康状态智能评估[J]. 中国医疗设备, 2022, 37(7): 19-25.
HAO Jingyu1, CHEN Yi2, WU Shuicai1. Intelligent Evaluation of Fetal Health Status Based on Stacking Ensemble Model. China Medical Devices, 2022, 37(7): 19-25.