ABSTRACT
Background: Recurrence can worsen conditions and increase mortality in ICH patients. Predicting the recurrence risk and preventing or treating these patients is a rational strategy to improve outcomes potentially. A machine learning model with improved performance is necessary to predict recurrence. Methods: We collected data from ICH patients in two hospitals for our retrospective training cohort and prospective testing cohort. The outcome was the recurrence within one year. We constructed logistic regression, support vector machine (SVM), decision trees, Voting Classifier, random forest, and XGBoost models for prediction. Results: The model included age, NIHSS score at discharge, hematoma volume at admission and discharge, PLT, AST, and CRP levels at admission, use of hypotensive drugs and history of stroke. In internal validation, logistic regression demonstrated an AUC of 0.89 and precision of 0.81, SVM showed an AUC of 0.93 and precision of 0.90, the random forest achieved an AUC of 0.95 and precision of 0.93, and XGBoost scored an AUC of 0.95 and precision of 0.92. In external validation, logistic regression achieved an AUC of 0.81 and precision of 0.79, SVM obtained an AUC of 0.87 and precision of 0.76, the random forest reached an AUC of 0.92 and precision of 0.86, and XGBoost recorded an AUC of 0.93 and precision of 0.91. Conclusion: The machine learning models performed better in predicting ICH recurrence than traditional statistical models. The XGBoost model demonstrated the best comprehensive performance for predicting ICH recurrence in the external testing cohort.
ABSTRACT
BACKGROUND: In recent years, with the further research of human genome scanning, the relationship between matrix metalloproteinase-9 (MMP-9) gene polymorphisms and many diseases has aroused increased attention. But there is little research on the relationship between MMP-9 gene polymorphisms and ischemic stroke (IS). This study was conducted to evaluate the relationships between the rs3918242 and rs17577 polymorphisms in the MMP-9 gene and IS in a Chinese population. METHODS: 152 cases of IS patients and 152 healthy controls were enrolled in this study. All subjects were genotyped for the MMP-9 rs3918242 and rs17577 polymorphisms by polymerase chain reaction (PCR) coupled with restriction fragment length polymorphism (RFLP) and restriction analysis. RESULTS: Rs3918242 showed genotypes TT, TC, and CC, and rs17577 exhibited genotypes AA, AG, and GG. The MMP-9 polymorphisms rs3918242 and rs17577 exhibited complete linkage. Our study found there was no significant difference in genotype and allele between rs3918242 and rs17577 between patients and controls. The MMP-9 gene rs3918242 and rs17577 polymorphisms are not significantly correlated with IS risk. Genetic polymorphisms vary among ethnic and regional populations. CONCLUSION: Our results suggest that MMP-9 rs3918242 is completely linked to rs17577, while the rs3918242 and rs17577 polymorphisms are not significantly associated with the risk of IS. Genetic polymorphisms vary among ethnic and regional populations.
