Data analytics approaches for breast cancer survivability: Comparison of data mining methods

Eyyub Y. Kibis, I. Esra Büyüktahtakin, Ali Dag

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

In the early stages of breast cancer, surgery, chemotherapy, and radiotherapy are considered effective methods to remove a cancerous tumor that is detected in the breast area and on the lymph nodes. However, undetected cancer cell remnants on the breast tissue and lymph nodes, inefficient treatment methods, as well as the patient's health condition may impact the patient's lifetime expectancy. In this study, given a set of explanatory variables that include the patient's demographics, health condition, and cancer treatment regimen, our objective is to investigate the performance of four different machine learning methods including an artificial neural network (ANN), classification and regression tree (C&RT), logistic regression, and Bayesian belief network (BBN). We utilize these four methods with a ten-fold cross validation in order to predict the ten-year survivability of a breast cancer patient after initial diagnosis. The results of each method are compared with respect to accuracy, sensitivity, specificity, and area under the curve (AUC) metrics. We observe that the logistic regression method shows better performance compared to the others with respect to the AUC metric. In all prediction models, the stage of the cancer is the most important predictor of breast cancer survivability.

Original languageEnglish (US)
Title of host publication67th Annual Conference and Expo of the Institute of Industrial Engineers 2017
EditorsHarriet B. Nembhard, Katie Coperich, Elizabeth Cudney
PublisherInstitute of Industrial Engineers
Pages591-596
Number of pages6
ISBN (Electronic)9780983762461
StatePublished - 2017
Externally publishedYes
Event67th Annual Conference and Expo of the Institute of Industrial Engineers 2017 - Pittsburgh, United States
Duration: May 20 2017May 23 2017

Publication series

Name67th Annual Conference and Expo of the Institute of Industrial Engineers 2017

Other

Other67th Annual Conference and Expo of the Institute of Industrial Engineers 2017
Country/TerritoryUnited States
CityPittsburgh
Period5/20/175/23/17

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Keywords

  • Artificial neural network
  • Bayesian belief network
  • Breast cancer
  • Data mining
  • Decision tree

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