Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery.

ABSTRACT:

Background

Surgical resection of pheochromocytoma may lead to high risk factors for intraoperative hemodynamic instability (IHD), which can be life-threatening. This study aimed to investigate the risk factors that could predict IHD during pheochromocytoma surgery by data mining.

Method

Relief-F was used to select the most important features. The accuracies of seven data mining models (CART, C4.5, C5.0, and C5.0 boosted), random forest algorithm, Naive Bayes and logistic regression were compared, the cross-validation, hold-out, and bootstrap methods were used in the validation phase. The accuracy of these models was calculated independently by dividing the training and the test sets. Receiver-Operating Characteristic curves were used to obtain the area under curve (AUC).

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC7370474 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature