The New Method of Feature Selection for Intradialytic Hypotension Prediction using Machine learning

Intradialytic hypotension (IDH) is a common complication and make patient get brain and heart disease. This study aimed to use the new method of feature selection to improve the performance of IDH prediction. We used feature importance by a different model to sort from high to low one by one, remove the lowest feature importance, and follow the rule again. Therefore, we obtain the combination of feature values that have the highest accuracy for predictive power. After applying REFCV with linear regression, the AUC of ROC increased to 0.966 (2-layers LSTM) and 0.965 (3-layers LSTM). The feature selection method we designed resulted in a better prediction ability in the model.