Intelligent classification of antepartum cardiotocography model based on deep forest

Abstract

Intelligent classification of antepartum cardiotocography (CTG) can assist obstetricians to make clinical decisions, which is helpful to improve the accuracy of fetal abnormality detection. However, most of the existing machine learning based classification models fail to meet the clinical requirements. In this work, a method is proposed to improve the classification accuracy by using Deep Forest (DF) algorithm. Principal component analysis and visualization were adopted to mine the distribution characteristics of the CTG data. After data preprocessing, deep forest multi-granularity scanning phase was used to explore the connection between the case characteristic. Then the cascade forest phase, which was designed to integrate Random Forest (RF), Weighted Random Forest (WRF), Completely Random Forest (CRF) and Gradient Boosting Decision Tree (GBDT) as the basic classifiers, performed deep iterations and finally got the best performance model. Compared with the traditional machine learning models, deep neural network and the state-of-art CTG classification models, the results show that the accuracy value, average F1 value and Area Under the Curve (AUC) value were 92.64 %, 92.01 % and 0.990 respectively in the external public data set, and were 91.64 %, 88.92 % and 0.9493 respectively in the internal private data set, which were the most excellent among all comparison models. In conclusion, the proposed DF model is effective and feasible, and has a good application prospect in the intelligent assessment of antenatal fetal health status.