Prediction of high-risk pregnancy based on machine learning algorithms

This study explores the application of machine learning algorithms in predicting high-risk pregnancy among expectant mothers, aiming to construct an efficient predictive model to improve maternal health management. The study is based on the maternal health risk dataset (MHRD) from Bangladesh, covering multiple hospitals, community clinics, and maternal healthcare centers, and encompassing health data from 1014 pregnant women. Six machine learning algorithms-multilayer perceptron (MLP), logistic regression (LR), decision tree (DT), random forest (RF), eXtreme Gradient Boosting (XGBoost), and support vector machine (SVM)-are employed to construct predictive models. It is worth noting that MLP demonstrates superior performance compared with the other five algorithms. By applying the MLP method, the study successfully established an efficient pregnancy risk prediction model. The model evaluation results indicate that it has high accuracy in predicting pregnancy risks, with an overall accuracy rate of 82%, and particularly high accuracy in high-risk predictions, reaching 91%. With the computational support of an NVIDIA GPU RTX3050Ti, the model demonstrated excellent data processing capabilities, capable of predicting and processing 500 sets of data items per second. This study not only showcases the enormous potential of machine learning technology in the healthcare field, especially in the rapid and accurate identification of high-risk pregnancies, providing a powerful decision-support tool for medical professionals, but also offers significant reference value for future research in this area.