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: 101 patients with acute paraquat poisoning admitted to 6 hospitals from March 2020 to March 2022 were selected for this study. After expiry of the treatment period (one year of follow-up for survivors and up to the time of death for deceased patients) and they were categorized into the survival group (n = 37) and death group (n = 64). The biochemical indexes of the patients were analyzed, and a prognosis prediction model 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 these features' survival and death groups (P < 0.05). Multivariate logistic analysis identified four features 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 precious in constructing a prognosis prediction model and visualization system for acute paraquat poisoning, which can help clinical prognosis prediction of patients with paraquat poisoning.
Keywords: Machine learning model; acute paraquat poisoning; prediction model; visualization system construction.
© The Author(s) 2024.