Artificial Intelligence in Early Diagnosis of Preeclampsia

Abstract

Background: Every day, 810 women die of preventable causes related to pregnancy and childbirth worldwide, and preeclampsia is among the top three causes of maternal deaths. Aim: To develop a diagnostic system with artificial intelligence for the early diagnosis of preeclampsia. Methods: This retrospective study included pregnant women who were screened for the inclusion criteria on the hospital’s database, and the sample consisted of the data of 1158 pregnant women diagnosed with preeclampsia and 9194 pregnant women who were not diagnosed with preeclampsia at Kahramanmaras Necip Fazıl City Hospital Gynecology and Pediatrics Additional Service Building, Kahramanmaras/Turkey. The statistical analysis was performed using the Statistical Package for social sciences (SPSS) version 22 for windows. Artificial intelligence models were created using Python, scikit-learn, and TensorFlow. Results: The model achieved 73.7% sensitivity (95% confidence interval (CI): 70.2%–77.1%) and 92.7% specificity (95% CI: 91.7%–93.6%) on the test set. Furthermore, the model had 90.6% accuracy (95% CI: 90.1% - 91.1%) and an area under the curve (AUC) value of 0.832 (95% CI: 0.818-0.846). The significant parameters in predicting preeclampsia in the model were hemoglobin (HGB), age, aspartate transaminase level (AST), alanine transferase level (ALT), and the blood group. Conclusion: Artificial intelligence is effective in the prediction and diagnosis of preeclampsia.