Comparison Of Data Mining Algorithms For Mammogram Classification
This paper describes a breast cancer classification performance trade-off analysis using two computational
intelligence system. The proposed system has been implemented in four stages: (a) Region of interest (ROI) which identifies
suspicion regions, (b) feature extraction stage locally processed image (ROI) to compute important features of each breast
cancer. (c) Feature selection stage by using forward stepwise linear regression method (FSLR). (d) Classification stage
which classifies between cancer and non-cancer case. In the classification stage we are applying two computational
intelligence paradigms. K- Nearest Neighbor and Naïve Bayes Algorithm are used for classification of data whether it is
cancer or non- cancer.
Keywords— Naïve Bayes, k- Nearest Neighbor, Region of Interest, Feature Extraction.