Smart Prediction of Circulatory Failure: Machine Learning for Early Detection of Patient Deterioration

Circulatory failure, also known as shock, is a critical condition that can have serious consequences for one’s health. Early detection and timely intervention are crucial for improving patient outcomes. Machine learning (ML) models have shown promise in predicting circulatory failure based on clinical data. In our study, we examined different machine learning (ML) models to predict circulatory failure in patients who were admitted to the intensive care unit (ICU) with suspected circulatory problems. The ML model we developed used various algorithms like random forest, LG, XGB, Decision Tree, and SVM. It was trained using clinical information such as vital signs, laboratory values, and demographic data. By considering these different factors, our model aimed to accurately predict circulatory failure in critically ill patients. Furthermore, we employed feature selection techniques such as SelectKBest to identify the most informative clinical variables for prediction. Using receiver operating characteristic (ROC) curves and area under the curve (AUC) values, we evaluated the model’s performance. Our results show that machine learning models can contribute significantly to predicting circulatory failure in patients at high risk of deterioration.