Rocio Garcia-Retamero, Edward T Cokely, Ulrich Hoffrage
Visual aids improve diagnostic inferences and metacognitive judgment calibration
Frontiers in psychology
patients alphafetoprotein afppositive hepatocellular carcinoma hcc aggressive biological behavior poor prognosis therefore survival time one greatest concerns patients afppositive hcc study aimed demonstrate utilization six machine learning mlbased prognostic models predict overall survival patients afppositive hcc data patients afppositive hcc extracted surveillance epidemiology end results database six ml algorithms extreme gradient boosting xgboost logistic regression lr support vector machine svm random forest rf knearest neighbor knn decision tree id3 used develop prognostic models patients afppositive hcc one year three years five years area receiver operating characteristic curve auc confusion matrix calibration curves decision curve analysis dca used evaluate model total 2038 patients afppositive hcc included analysis 1 3 5year overall survival rates 607 289 143 respectively seventeen features regarding demographics clinicopathology included six ml algorithms generate prognostic model xgboost model showed best performance predicting survival 1year train set auc 0771 test set auc 0782 3year train set auc 0763 test set auc 0749 5year train set auc 0807 test set auc 0740 furthermore 1 3 5year survival prediction accuracy training test sets 0709 0726 0721 0726 0778 0784 xgboost model respectively calibration curves dca exhibited good predictive performance well xgboost model exhibited good predictive performance may provide physicians effective tool early medical intervention improve survival patients