Construction of prognosis prediction model and visualization system of acute paraquat poisoning based on improved machine learning model

Abstract

Objective: This study aims to develop a prognosis prediction model and visualization system for acute paraquat poisoning based on an improved machine learning model. Methods: A total of 101 patients with acute paraquat poisoning admitted to 6 hospitals from March 2020 to March 2022 were selected for this study. The patients were divided into two groups, the survival group (n=37) and the death group (n=64), based on treatment results. The biochemical indexes of the patients were analyzed, and a prognosis prediction model for acute paraquat poisoning was constructed using HHO-XGBoost, an improved machine learning algorithm. Multivariate logistic analysis was used to verify the value of the self-screening features in the model. Results: Seven features were selected in the HHO-XGBoost model, including oral dose, serum creatinine, alanine aminotransferase (ALT), white blood cell (WBC) count, neutrophil count, urea nitrogen level, and thrombin time. Univariate analysis showed statistically significant differences between the survival group and death group for these features (P<0.05). Multivariate logistic analysis identified four features that were significantly associated with prognosis-serum creatinine level, oral dose, ALT level, and WBC count - indicating their critical significance in predicting outcomes. Conclusion: The HHO-XGBoost model based on machine learning is highly valuable in constructing a prognosis prediction model and visualization system for acute paraquat poisoning, which can provide important help for clinical prognosis prediction of patients with paraquat poisoning.