Presentation of hidden knowledge from a localbreast cancer dataset by the classification and regression trees

Hadi  LotfnezhadAfshar; Lili  Rahmatnejad; Bahlol  Rahimi; Hamid Reza  Khalkhali

Journal of Clinical Research in Paramedical Sciences

The Official Journal of Paramedical School, KUMS

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Presentation of hidden knowledge from a localbreast cancer dataset by the classification and regression trees

Author(s):

Hadi LotfnezhadAfshar ¹,

Lili Rahmatnejad²,

Bahlol Rahimi¹,

Hamid Reza Khalkhali^3,*

1Department of Health Information Management, Health Information Technology Department, School of Paramedicine, Urmia University of Medical Sciences, Urmia, Iran

2Department of Midwifery, School of Nursing & Midwifery, Urmia University of Medical Sciences, Urmia, Iran

3Department of Biostatistics, Patient Safety Research Center, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran

Journal of Clinical Research in Paramedical Sciences:Vol. 6, issue 2; e81268

Published online:Jul 24, 2017

Article type:Research Article

Received:Nov 20, 2016

Accepted:May 03, 2017

How to Cite:Hadi LotfnezhadAfshar Lili RahmatnejadBahlol RahimiHamid Reza KhalkhaliPresentation of hidden knowledge from a localbreast cancer dataset by the classification and regression trees.J Clin Res Paramed Sci.6(2):e81268.

Abstract

Introduction: The using of standard knowledge discovery methods such as decision trees, in context ofthe breast cancer has been studied. Presentation of undiscovered relationship among data in formats such as: visualization and formulating are the reasons of decision trees popularity. An algorithm from this group that has not been used in the previous published papers, applied in current study.

Methods: A dataset included data about 569 patients’ records between the years 2007 and 2010 was used. The missing data handling method was multiple imputation (MI). IBM statistics 21 was the used software for running MI and developing the model. The developed model was evaluated against the criteria such as: accuracy, sensitivity and specificity.

Results: A decision tree with seventeen nodes produced by the model. A set of clinically meaningful if-then rules were produced from nine nodes. It was clear from these rules that the variable that showed the stage of cancer was the most important variable to predict living probability of breast cancer. The performance of produced model for criteria (sensitivity, specificity and accuracy) was: 93.5, 53 and 80.3 percentage respectively.

Conclusion: The model created in current study as the first model in living probability of breast cancer revealed practical undiscovered rules from a not large dataset.

Keywords

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