Machine Learning-Based Modeling of Clinical Diagnosis and Treatment of Patients With Hemorrhagic Stroke

Abstract

Stroke includes both hemorrhagic and ischaemic stroke, and with the rising incidence of stroke, the mortality rate of hemorrhagic stroke is higher than that of ischaemic stroke, accounting for 15% of the stroke mortality rate. In this area, clinically intelligent diagnosis and treatment plays an important role. By integrating imaging features, patient clinical information, treatment plans and diagnosis, accurate personalized efficacy assessment and prognosis prediction can be achieved. In this study, machine learning models (Random Forest, XGBoost, logistic regression, LGBoost, and AdaBoost) for the exploration of factors associated with the risk of haematoma expansion (HE) were developed based on patients' diagnostic data. mRS scores were used to assess the prognostic status of the patients, Principal Component Analysis was used for data dimensionality reduction, and Spearman Correlation Analysis was used to analyze the features' direct correlation. Five machine learning models were applied to predict the probability of HE and the prognosis of hemorrhagic stroke in patients. The models were tuned using grid search and ten-fold cross-validation methods to obtain more accurate predictions. The results of the study showed that the mRS index and factors such as history of diabetes, history of coronary heart disease, haematoma volume and age were closely related to the prognosis of patients. Among them, RF and XGBoost performed well in predicting the probability of HE, with the area under the ROC curve reaching 0.98, while LGBoost performed best in predicting the prognostic status of hemorrhagic stroke patients.