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 wa...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 20; no. 1; pp. 1 - 14 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
7/20/2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144673838&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144673838 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 7/20/2020 vid: 20 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 144673838 144673838 NLM32690077 10.1186/s12911-020-01180-4 NLM32690077 144673838 ppf: 1 ppct: 13 formats: tig: atl: Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery. aug: au: Zhao, Yueyang Fang, Li Cui, Lei Bai, Song affil: Library of Shengjing Hospital of China Medical University, 110004, Shenyang, China sug: subj: Data Mining Adrenal Gland Neoplasms Pheochromocytoma Hemodynamics Male Female Middle Age Probability Adult Aged Scales Middle Aged: 45-64 years Adult: 19-44 years Aged: 65+ years Male Female ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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