Comparative Study of Classification Techniques for Breast Cancer Diagnosis
DOI:
https://doi.org/10.26438/ijcse/v7i1.234240Keywords:
Classification Techniques, Feature Selection, k-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), Bayesian Network (BN), WEKAAbstract
Classification techniques in Machine Learning are implemented on datasets. In this work, the cancer datasets are used for the classification purpose and collected from UCI Machine Learning repository. There are two types of datasets of breast cancer. Both the datasets are varying by their number of features available across the datasets. This paper presents the implementation and comparative study of major and popular classification techniques such as Decision Tree, k-Nearest Neighbour, Support Vector Machine, Bayesian Network and Naïve Bayes under WEKA environment for accuracy based on evaluation of performance metrics. This paper evaluates that the Bayesian Network gives the best accuracy with less featured dataset while Support Vector Machine gives best accuracy for more featured dataset.
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