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 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.
ObjectiveTo evaluate the changes of left ventricular structure and function by echocardiography and its grading of left ventricular diastolic function in patients with mitral valve prolapse treated by minimally invasive mitral valve repair.MethodsBy retrospective analysis, 37 patients including 25 males and 12 females aged 53.49±11.02 years with mitral valve prolapse who underwent minimally invasive mitral valve repair were as an operation group, and 34 healthy persons including 19 males and 15 females aged 54.26±8.33 years matched by age and sex were selected as a control group. Ultrasound parameters of every participant were routinely collected before operation, 1 month, 3 months, 6 months and 1 year after operation, and left ventricular diastolic function was graded. The ultrasound parameters between the two groups were compared.ResultsThe diameters of left ventricular end systolic and diastolic phase, left atrial diameter and left ventricular volume in the operation group were significantly smaller than those before operation. The diameters of left ventricle and left atrium after operation were significantly shorter than those before operation, but they were still larger than those of the control group. The ejection fraction value decreased significantly at one month after the operation and then returned to normal level. The incidence of left ventricular diastolic dysfunction at 6 months and 1 year after operation was significantly lower than that before operation (P<0.05).ConclusionMinimally invasive repair for patients with mitral valve prolapse can significantly improve systolic and diastolic functions of left ventricle while reconstructing left atrial and left ventricular structures.