QIN Yali 1 , YAO Liping 2 , YUAN Ling 3 , CHEN Sheng 1
  • 1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
  • 2. Department of Ultrasound, Shanghai First Maternity and Infant Health Hospital, Shanghai 201204, P. R. China;
  • 3. Department of Obstetrics, Shanghai First Maternity and Infant Health Hospital, Shanghai 201204, P. R. China;
CHEN Sheng, Email: chnshn@163.com
Induction of labor; Maximal information coefficient; Support vector machine; Fully connected neural network; Predictive model; Overfitting
10.7507/1001-5515.202403033
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Because of the diversity and complexity of clinical indicators, it is difficult to establish a comprehensive and reliable prediction model for induction of labor (IOL) outcomes with existing methods. This study aims to analyze the clinical indicators related to IOL and to develop and evaluate a prediction model based on a small-sample of data. The study population consisted of a total of 90 pregnant women who underwent IOL between February 2023 and January 2024 at the Shanghai First Maternity and Infant Healthcare Hospital, and a total of 52 clinical indicators were recorded. Maximal information coefficient (MIC) was used to select features for clinical indicators in the process of induced labor to reduce the risk of overfitting caused by high-dimensional features. Then, based on the features selected by MIC, the support vector machine (SVM) model based on small samples was compared and analyzed with the fully connected neural network (FCNN) model based on large samples in deep learning, and the receiver operating characteristic (ROC) curve was given. By calculating the MIC score, the final feature dimension was reduced from 55 to 15, and the area under curve (AUC) of the SVM model was improved from 0.872 before feature selection to 0.923. Model comparison results showed that SVM had better prediction performance than FCNN. This study demonstrates that SVM successfully predicted IOL outcomes, and the MIC feature selection effectively improves the model’s generalization ability, making the prediction results more stable. This study provides a reliable method for predicting the outcome of induced labor with potential clinical applications.