Fetal Health Classification Based on Machine Learning

摘要

Cardiotocogram (CTG) is the most widely used in the clinical routine evaluation of the main approach to detect fetal state. In this paper, twelve machine learning single models have firstly experimented on CTG dataset. Secondly, the soft voting integration method is used to integrate the four best models to build the Blender Model, and compared with the stacking integration method. Compared with the traditional machine learning models, the model proposed in this paper performed excellently in various Classification Model evaluations, with an accuracy rate of 0.959, an AUC of 0.988, a recall rate of 0.916, a precision rate of 0.959, a F1 of 0.958 and a MCC of 0.886.