No abstract

Breast cancer, the most common of types of cancer that threatens human life more specifically women can be diagnosed with classification techniques of data mining. This work is an extension of earlier implementation of breast cancer analysis of the author through iterative linear regressive classifier. The objective of this study is to make cent percent prediction accuracy in the diagnosis of breast cancer over the traditional Wisconsin dataset. The novelty of the paper includes the benefits of the previous ILRC and also takes the advantages of AI. The results of the proposed work are evaluated against the randmeasure and have proven that the results yield cent percent prediction accuracy in diagnosing breast cancer.