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Classifiers’ Accuracy Based on Breast Cancer Medical Data and Data Mining Techniques

Journal Title: International Journal of Advanced Biotechnology and Research. - Year 2016, Vol 7, Issue 2

Abstract

ABSTRACT Knowledge Data Discovery (KDD) in Breast Cancer data set includes; data collection, data preprocessing, data mining, knowledge extraction, patterns validation, and results visualization. Where, this disease is considered as one of the deadly diseases in the world. The research problem is that there are many machine learning classifiers with different levels of accuracy when applied to Breast Cancer data. The highest accuracy of a classifier model depends upon the nature and the size of the data used to establish this model. To help physicians select the most accurate classifier algorithm to be applied using this kind of data. The paper’s objective is to discover the most accurate classifier among all Data Mining classifiers based on Breast Cancer data set and the WEKA software. The results compared with previous results in other researches and show highly accurate results. It was found that the Bayes Net classifier model is the best classifier among the other types.

Authors and Affiliations

Mohammed Abdullah Hassan Al-Hagery

Keywords

Breast Cancer Disease Medical Data Sets Data Mining Models Classification Accuracy Decision Making Quality of Data.
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  • EP ID EP101780
  • DOI -
  • Views 120
  • Downloads 0

How To Cite

Mohammed Abdullah Hassan Al-Hagery (2016). Classifiers’ Accuracy Based on Breast Cancer Medical Data and Data Mining Techniques. International Journal of Advanced Biotechnology and Research., 7(2), 760-772. https://europub.co.uk/articles/-A-101780

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