Machine Learning Predicts Prognosis of Intracerebral Hemorrhage
Intracerebral Hemorrhage(ICH)is one of the main causes of death and disability in Chinese residents.To predict the functional prognosis and identify its influencing factors is of guiding significance for clinical treatment.In this paper,seven machine learning models were established to predict the three-level prognosis of patient by using high-dimensional and small-sample ICH data.Three feature selection methods(US,RFE-RF,RFE-NB)were used on 22 clinical indicators,and three dimensionality reduction methods(PCA,MDS,UMAP)were used on 84 imaging indicators.Compare the prediction performance(Accuracy,F1,Kappa,HUM)of different combinations of feature selection and dimensionality reduction for seven machine learning classifiers(LASSO/Ridge/ENet penalized trinomial logit models,SVM,RF,XGBoost,ANN).Then,the Wilcoxon signed-rank test is used to compare classifier performance differences of the classifiers.Experiment results show that the RF is the highest accuracy without considering feature selection and dimensionality reduction(benchmark).In terms of the number of better than the benchmark RF accuracy,the effectiveness of dimensionality reduction was ranked as MDS,PCA and UMAP,and RFE-RF outperformed US and RFE-NB.The prediction accuracy of LASSO and XGBoost is mainly increased after feature selection or dimensionality reduction.The Wilcoxon signed rank test was performed on the combinations that outperformed the benchmark RF prediction performance.The Macro-F1 and Weighted-F1 of UMAP+US+LASSO were superior to the benchmark RF.The HUM of MDS23+SVM,US+SVM,RFE-RF+SVM and RFE-NB+SVM is better than that of the benchmark RF.The common variables selected by the three feature selection methods of clinical data are low pressure,high-low pressure ratio and diabetes history.
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