Diagnosing Breast Cancer using Support Vector Machine and Multi-Classifiers

One and only primary reason of demise amid females is Breast cancer or Carcinoma. Initial identification of irregularities in breast definitely assists the radiologist to diagnose and detect the breast cancer disease. In this paper, classification of type of cancer is proposed to diagnose the breast cancer from classification dataset. Data set is given to different classifiers like Support Vector Machine, Naïve-Bayes, Simple Logistics, Neural Network-MLP, Random Forest and Decision Trees. Cross Validation performed, leading to training and testing the model. Classification accuracy is obtained and results are measured on few parameters. The results of all the classifiers obtained are evaluated. Support Vector Machine offers high accurateness and F-score in comparison with multi-classifiers.