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Using Frequent Itemset Mining for Breast Cancer Detection
Objectives: This paper introduces an improvement over the use of Artificial Neural Networks (ANN) for breast cancer detection. Methods: It suggests use of frequent pattern mining for minimizing the dimensions of breast cancer database. After reduction step, the database is then input to an ANN for classification. Findings: We have shown through experimentation that the proposed model not only reduces the input database dimensions but also produces better classification results. Application: The proposed model will be highly beneficial in the field of medicine and will help in precise detection of breast cancer.
Artificial Neural Network, Association Rule Mining, Breast Cancer Detection, Dimension Reduction, Frequent Itemset Mining
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