Survival Prediction of Patients with Breast Cancer: Comparisons of Decision Tree and Logistic Regression Analysis.
Background: Breast cancer is the first cause of cancer-related deaths among women in Iran. Objectives: The aim of the present study was to compare the traditional statistical analysis and data mining technique as the research methods for identifying the prognostic factors regarding the survival time...
| Published in: | International Journal of Cancer Management Vol. 11; no. 7; pp. 1 - 9 |
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| Main Authors: | , , , , |
| Format: | research tables/charts Journal Article |
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Medical Journals Commission of the Ministry of Health & Medical Education
Jul2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130969664&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130969664 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 25384422 L6AI jtl: International Journal of Cancer Management issn: 25384422 maglogo: N pubinfo: dt: Jul2018 vid: 11 iid: 7 pid: 66482 pub: Medical Journals Commission of the Ministry of Health & Medical Education artinfo: ui: 130969664 130969664 130969664 10.5812/ijcm.9176 130969664 ppf: 1 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Survival Prediction of Patients with Breast Cancer: Comparisons of Decision Tree and Logistic Regression Analysis. aug: au: Momenyan, Somayeh Baghestani, Ahmad Reza Momenyan, Narges Naseri, Parisa Akbari, Mohammad Esmaeil affil: PhD Candidate in Biostatistics, Department of Biostatistics, Paramedical Sciences Faculty, Shahid Beheshti University of Medical Sciences, Tehran, Iran sug: subj: Breast Neoplasms Prognosis Survival Analysis Human Cancer Survivors Decision Trees Multiple Logistic Regression Data Analysis, Statistical Data Mining Algorithms Chi Square Test Academic Medical Centers Office Visits Age Factors Breast Neoplasms Diagnosis Histological Techniques Methods Neoplasm Grading Lymph Nodes Pathology Breast Neoplasms Surgery Breast Neoplasms Mortality Breast Neoplasms Pathology Menarche Hormone Therapy Iran ab: Background: Breast cancer is the first cause of cancer-related deaths among women in Iran. Objectives: The aim of the present study was to compare the traditional statistical analysis and data mining technique as the research methods for identifying the prognostic factors regarding the survival time of patients with breast cancer. Decision tree method is one of the predictive models that used in the medical field. The most used algorithms are classification and regression trees (CART), the quick, unbiased, efficient statistical tree (QUEST), Chi-square automatic interaction detector (CHAIDs) algorithm, and the C5.0 algorithm. Methods: We used data for 438 patients, who were referred to cancer research center in Shahid Beheshti University of Medical Sciences. The patients were visited and treated during 1992 to 2012 and followed up until October 2014. The data were analyzed by regression logistic and decision tree method. Six measures for evaluation of predictive performance of different models were used. Results: The C5.0 algorithm performed better than CHAID, QUEST, CART algorithms, and the logistic regression in predicting breast cancer survival. The multiple logistic regression results indicated that the factors of age at diagnosis, histologic grade, axillary lymph node status, and type of surgery were statistically significant with regard to the probability of death in patients with breast cancer. Moreover, based on C4.5 they reported that tumor size, age of menarche, hormonal therapy, axillary nodal status, and histological grade are the most prominent variables. Conclusions: The more precise methods can identify the more accurate predictors. The decision tree method was able to predict the probability of death more accurately compared with the conventional logistic regression. Some improvements for classical classification tree such as boosting and bagging have been developed in order to obtain better predictive performance. We suggest that the modern classification tree method in the breast cancer context be the focus of future studies. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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