The invention discloses a prediction device for cardiovascular adverse events of
percutaneous coronary intervention based on
machine learning. The prediction device is characterized in that a
cardiovascular event prediction model is saved in a memory, the
cardiovascular event prediction model comprises a trained XGboost model, a LightGBM model, an SVM model, an NN model and corresponding weights of each model. The operating process of the prediction device comprises the steps that to-be-detected clinical characteristic data is received, missing value filling is conducted on the to-be-detectedclinical characteristic data; correlation detection is conducted on the clinical characteristic data on which missing value filling is conducted, and the clinical characteristic value with a crossed relationship is removed; computation is conducted on the clinical characteristic data on which correlation detection is conducted using the trained XGboost model, the LightGBM model, the SVM model andthe NN model to obtain four prediction probabilities, and weighted summation is conducted on the four prediction probabilities to obtain the
prediction probability predicted by using the cardiovascular adverse event prediction model.