Research on the Design of Active Learning Algorithm based on Query-by-Committee for Intelligent Fetal Monitoring
Bin Quan, Manli Yang, Xia Li, Qinqun Chen, Guiqing Liu, Jiaming Hong, Zhifeng Hao, Li Li, Hang Wei · 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · 2021
The realization of intelligent fetal monitoring is helpful to timely detect fetal abnormality and save medical costs. However, the probability of misjudgment tends to be high when modeling the imbalanced clinical data directly. Moreover, reliable interpretation of cardiotocography (CTG) requires multiple obstetricians to annotate, which consumes excessive time and labor cost. In this paper, a K-means method based on adaptive Genetic Algorithm-Fl balanced diversity Weighted Query-by-Committee (QBC) algorithm (KGA-WQBC) was proposed to solve the above problems. The proposed active learning algorithm was improved from the following two aspects. Firstly, an adaptive KGA operator was designed to conduct a preliminary selection of a large number of unlabeled samples, which increased the accuracy of the committee. Then, the WQBC operator was designed to improve the measurement of the committee's disagreement degree after data exploration. Compared with other initial sample selection strategies and disagreement degree weighting means, the results showed that only 41% of the labeled instances were used to make the evaluation index as high as 98.02%, which had the best overall performance. In conclusion, the proposed KGA-WQBC algorithm is reasonable and feasible, and effectively solves the problem of imbalanced data and reduces the cost of medical personnel labeling for intelligent fetal monitoring.