Prediction of breast cancer using classification rule mining techniques in blood test datasets

Abstract

Mining provides useful information from the huge volume of the data stored in repositories. The present study focus on implementing five different algorithms using the data mining WEKA. The algorithm in the study includes Naive Bayes, Zero R, One R, J48 and Random Tree algorithm. All these well-known familiar algorithms are used in classification rule mining techniques. Datasets are collected from the Arignar Anna Cancer Institute for implementing the Data Mining. These collected datasets are preprocessed and then used for implementing the algorithm. The different types of algorithms are executed using the collected datasets and, the results are shown in separate window as well as graphical or tree manner based on it applicability.

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Cite this paper

@article{Muthuselvan2016PredictionOB, title={Prediction of breast cancer using classification rule mining techniques in blood test datasets}, author={S. Muthuselvan and K. Soma Sundaram and Prabasheela}, journal={2016 International Conference on Information Communication and Embedded Systems (ICICES)}, year={2016}, pages={1-5} }