Performance Evaluation of two Prediction Models for Breast Cancer Metastasis Based on Data Mining Techniques, A Comparison Study

Authors

  • Najme Nazeri1 1- Dept. Medical Informatics, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran.
  • Ali Reza Atashi1 1- Dept. Medical Informatics, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran.
  • Sara Dorri2 2- Dept. of Medical Informatics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Ebrahim Abbasi3 3- Student Research Committee, School of Medical Sciences, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Mohsen Alijani1 1- Dept. Medical Informatics, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran.
  • Mohsen Goli1 1- Dept. Medical Informatics, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran.

DOI:

https://doi.org/10.22100/jkh.v12i1.1613

Keywords:

Data mining, Breast cancer, Metastasis, Prediction

Abstract

Introduction: Defining the metastasis processes and what are the most effecting factors on improve the survival of patients and hopefully treating them. We aim to investigate and defining the factors predict breast cancer metastasis using data mining techniques. Data mining is the technique and tool of knowledge discovery from the big data. Nowadays data mining is spreading rapidly in several areas of research and business. In medicine, diagnosis of diseases is one of the fruitful and highly spreading filed of data mining.

Methods: There were 2025 usable records in ACECR Breast Disease Center’s data base after data preparation. We try to uncover the patterns that would help the prediction of metastasis factors using CHAID and Artificial Neural Network.

Results: After implementing mentioned algorithms, the tumor stage, surgery type and pathology results, the most important variables in metastasis prediction.

Conclusion: Comparing the algorithms execution revealed that, Artificial Neural Network, CHAID are convenient prediction models for breast cancer metastasis.

Author Biography

  • Najme Nazeri1, 1- Dept. Medical Informatics, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran.
    کارشناس ارشد مدیریت اطلاعات، عضو گروه پژوهشی انفورماتیک سرطان

Published

2017-05-07

Issue

Section

Original Article(s)

How to Cite

Performance Evaluation of two Prediction Models for Breast Cancer Metastasis Based on Data Mining Techniques, A Comparison Study. (2017). Knowledge and Health in Basic Medical Sciences, 12(1), page:36-42. https://doi.org/10.22100/jkh.v12i1.1613

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