Prediction of severe preeclampsia in machine learning

Abstract

This study aimed to find out the blood data characteristics of patients and explore the correlation between severe preeclampsia and blood index value. Provide assistance for the early attention direction of severe preeclampsia diagnosis and treatment. 19,653 pregnant women presenting to the West China Second University Hospital, Sichuan University from January 2017 to April 2019. After screening, a total of 248 patients, 124 severe preeclampsia cases, and 124 controls were selected for this study. Forty-three blood examination variables were obtained from routine blood work, hepatic, renal and coagulation function examination. Light gradient boosting machine (light GBM), decision tree and random forest were used for date diving. We randomly divided 35% of the original data as a testing set to conduct internal validation of the performance of the prediction model. The area under receiver operating characteristic curve (AUC) was used as the main score to compare the three methods. Finally, a binary classification light GBM model based on aspartate aminotransferase, direct bilirubin and activated partial thromboplastin time ratio can predict severe preeclampsia with sensitivity of 88.37%, specificity of 77.27%, AUC of 89.74% and positive predictive value of 65.96%. We believe relevant quantifiable indicators can establish an effective prediction model, which can provide guidance for early detection and prevention of severe preeclampsia.

Keywords

• Preeclampsia
• Screening
• Prediction
• Blood examination
• Data characteristics