A Fetal Health Classification Approach Based on Broad Learning System
Mingxuan Li, Yifeng Lin, Yuer Yang · Journal of Physics Conference Series · 2024
Abstract Fetal health problems are still a serious issue nowadays, affecting mothers and their children. The rate of mortality caused by fetal health problems is notably high in some areas of the earth, especially in lower-income countries. At present, besides the conventional clinical assessment, the methods of fetal health classification are mainly traditional machine learning and deep learning, such as KNN, SVM, Logistic Regression, and Naive Bayes, with the problems of low precision or long training time. In this study, we apply the broad learning system (BLS) to fetal health classification. The data set is sourced from the UCI ML repository database, which is made available by the Biomedical Engineering Institute and the University of Porto’s Faculty of Medicine. With the training accuracy of 92%, training time of 0.52s, and testing accuracy of 90%, the experimental results that highly satisfactory on BLS in the experiment.