Predicting quality of life after breast cancer surgery using ANN-based models: performance comparison with MR.
Purpose: The goal was to develop models for predicting long-term quality of life (QOL) after breast cancer surgery.Methods: Data were obtained from 203 breast cancer patients who completed the SF-36 health survey before and 2 years after surgery. Two of the models used to predict QOL after surgery w...
| Publicado en: | Supportive Care in Cancer Vol. 21; no. 5; pp. 1341 - 1351 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
May2013
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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=104261518&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104261518 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09414355 O1J jtl: Supportive Care in Cancer issn: 09414355 maglogo: N pubinfo: dt: May2013 vid: 21 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104261518 NLM23203653 2012065135 10.1007/s00520-012-1672-8 NLM23203653 104261518 ppf: 1341 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Predicting quality of life after breast cancer surgery using ANN-based models: performance comparison with MR. aug: au: Tsai, Jinn-Tsong Hou, Ming-Feng Chen, Yao-Mei Wan, Thomas T H Kao, Hao-Yun Shi, Hon-Yi affil: Department of Computer Science, National Pingtung University of Education, 4-18 Min-Sheng Road, Pingtung, 900, Taiwan, Republic of China. sug: subj: Breast Neoplasms Surgery Models, Statistical Neural Networks (Computer) Quality of Life Female Human Prospective Studies Regression Time Factors Short Form-36 Health Survey (SF-36) Questionnaires Female ab: Purpose: The goal was to develop models for predicting long-term quality of life (QOL) after breast cancer surgery.Methods: Data were obtained from 203 breast cancer patients who completed the SF-36 health survey before and 2 years after surgery. Two of the models used to predict QOL after surgery were artificial neural networks (ANNs), which included one multilayer perceptron (MLP) network and one radial basis function (RBF) network. The third model was a multiple regression (MR) model. The criteria for evaluating the accuracy of the system models were mean square error (MSE) and mean absolute percentage error (MAPE).Results: Compared to the MR model, the ANN-based models generally had smaller MSE values and smaller MAPE values in the test data set. One exception was the second year MSE for the test value. Most MAPE values for the ANN models ranged from 10 to 20 %. The one exception was the 6-month physical component summary score (PCS), which ranged from 23.19 to 26.86 %. Comparison of criteria for evaluating system performance showed that the ANN-based systems outperformed the MR system in terms of prediction accuracy. In both the MLP and RBF networks, surgical procedure type was the most sensitive parameter affecting PCS, and preoperative functional status was the most sensitive parameter affecting mental component summary score.Conclusion: The three systems can be combined to obtain a conservative prediction, and a combined approach is a potential supplemental tool for predicting long-term QOL after surgical treatment for breast cancer.Relevance: Patients should also be advised that their postoperative QOL might depend not only on the success of their operations but also on their preoperative functional status. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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