Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery

BMC Medical Informatics and Decision Making
Yueyang ZhaoSong Bai

Abstract

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. 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). Random forest had the highest AUC and accuracy values of 0.8636 and 0.8509, respectively. Then, we improved the random forest algorithm according to the classification of imbalanced data. Improved random forest model had the highest specificity and precision among all algorithms, including relatively higher sensitivity (recall) and the highest f1-score integrating recall and precision. The important attributes were body mass index, mean age, 24 h urine vanillylmandelic acid/uppe...Continue Reading

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Citations

Feb 16, 2021·Frontiers in Psychology·Yu-Min WangChun-Jung Chen

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Methods Mentioned

BETA
surgical resection
feature extraction
MDA

Software Mentioned

CART
Python
C50
e1071
R
rpart
RWeka

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