Predictive efficacy of machine-learning algorithms on intrahepatic cholestasis of pregnancy based on clinical and laboratory indicators.

Abstract

Objectives: Intrahepatic cholestasis of pregnancy (ICP), a condition exclusive to pregnancy, necessitates prompt identification and intervention to improve the perinatal outcomes. This study aims to develop suitable machine-learning models for predicting ICP based on clinical and laboratory indicators.

Methods: This study retrospectively analyzed data from 1092 pregnant women, with 537 diagnosed with ICP and 555 healthy cases as a control. Two study schemes were devised. For scheme 1, 62 indicators from the first period of gestation were utilized to establish predictive models. For scheme 2, 62 indicators from at least two periods of gestation were utilized to establish predictive models. Under each scheme, three different machine-learning models were developed based on the Arya Privacy Computing Platform, encompassing Support Vector Machine (SVM), Deep Neural Network (DNN), and Xgboost for Scheme 1, and Recurrent Neural Network (RNN), Long Short-Term Memory Network (LSTM), and Gated Recurrent Unit (GRU) for Scheme 2. The predictive efficacy of each model on ICP was evaluated and compared.

Results: Under Scheme 1, the cohort comprised 1092 pregnant women (537 with ICP, 555 healthy). The SVM model exhibited a sensitivity, specificity, and accuracy of 85.5%, 47.50%, and 67.90%, respectively, while DNN showed 65.70%, 92.70%, and 79.40%, respectively, and Xgboost achieved 65.60%, 81.90%, and 73.40%, respectively. In Scheme 2, 899 pregnant women were analyzed (466 with ICP, 433 healthy). RNN demonstrated a sensitivity, specificity, and accuracy of 97.60%, 82.10%, and 90.50%, respectively; LSTM presented 90.70%, 81.70%, and 86.60%, respectively; and GRU achieved 89.90%, 83.80%, and 89.40%, respectively.

Conclusion: DNN and RNN are the two most suitable models to predict ICP in a convenient and available way. It provides flexible choice for medical staff and helps them optimize the therapeutic strategies to meet different clinical setting and improve the clinical prognosis of ICP.