Classification of Cardiotocography Based on the Apriori Algorithm and Multi-Model Ensemble Classifier - Chen, Meng; Yin, Zhixiang (Frontiers in Cell and Developmental Biology, 2022)
Cardiotocography(CTG) recorded fetal heart rate and its temporal relationship with uterine contractions. CTG intelligent classification plays an important role in evaluating fetal health and protecting fetal normal growth and development throughout pregnancy. At the feature selection level, this paper uses Apriori algorithm to search frequent item sets for feature extraction. At the level of classification model, the combination model of AdaBoost and random forest with the highest classification accuracy is finally selected by comparing various models. The suspicious class data in CTG data set affects the overall classification accuracy. The number of suspicious class data is predicted by multi-model ensemble method. Finally, the data set is fused from three classifications to two classifications. The classification accuracy is 0.976 and the AUC is 0.98, which significantly improves the classification effect. In conclusion, the method used in this paper has high accuracy in model classification, which is helpful to improve the accuracy of fetal abnormality detection.