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Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery

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 w...

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Autores principales: Zhao, Yueyang, Fang, Li, Cui, Lei, Bai, Song
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7370474/
https://www.ncbi.nlm.nih.gov/pubmed/32690077
http://dx.doi.org/10.1186/s12911-020-01180-4
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author Zhao, Yueyang
Fang, Li
Cui, Lei
Bai, Song
author_facet Zhao, Yueyang
Fang, Li
Cui, Lei
Bai, Song
author_sort Zhao, Yueyang
collection PubMed
description 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). RESULT: 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/upper normal limit value, tumor size and enhanced computed tomography difference. CONCLUSIONS: The improved random forest algorithm may be useful in predicting IHD risk factors in pheochromocytoma surgery. Data mining technologies are being increasingly applied in clinical and medical decision-making, and provide continually expanding support for the diagnosis, treatment, and prevention of various diseases.
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spelling pubmed-73704742020-07-21 Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery Zhao, Yueyang Fang, Li Cui, Lei Bai, Song BMC Med Inform Decis Mak Research Article 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). RESULT: 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/upper normal limit value, tumor size and enhanced computed tomography difference. CONCLUSIONS: The improved random forest algorithm may be useful in predicting IHD risk factors in pheochromocytoma surgery. Data mining technologies are being increasingly applied in clinical and medical decision-making, and provide continually expanding support for the diagnosis, treatment, and prevention of various diseases. BioMed Central 2020-07-20 /pmc/articles/PMC7370474/ /pubmed/32690077 http://dx.doi.org/10.1186/s12911-020-01180-4 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Zhao, Yueyang
Fang, Li
Cui, Lei
Bai, Song
Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery
title Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery
title_full Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery
title_fullStr Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery
title_full_unstemmed Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery
title_short Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery
title_sort application of data mining for predicting hemodynamics instability during pheochromocytoma surgery
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7370474/
https://www.ncbi.nlm.nih.gov/pubmed/32690077
http://dx.doi.org/10.1186/s12911-020-01180-4
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