A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer

• A novel classification model based on fuzzy-rough nearest neighbor method.
• Fuzzy-rough instance selection.
• Consistency-based subset evaluation combined with re-ranking algorithm.
• The automated diagnosis of breast cancer with a classification accuracy of 99.71%.

Breast cancer is one of the most common and deadly cancer for women. Early diagnosis and treatment of breast cancer can enhance the outcome of the patients. The development of classification models with high accuracy is an essential task in medical informatics. Machine learning algorithms have been widely employed to build robust and efficient classification models. In this paper, we present a hybrid intelligent classification model for breast cancer diagnosis. The proposed classification model consists of three phases: instance selection, feature selection and classification. In instance selection, the fuzzy-rough instance selection method based on weak gamma evaluator is utilized to remove useless or erroneous instances. In feature selection, the consistency-based feature selection method is used in conjunction with a re-ranking algorithm, owing to its efficiency in searching the possible enumerations in the search space. In the classification phase of the model, the fuzzy-rough nearest neighbor algorithm is utilized. Since this classifier does not require the optimal value for K neighbors and has richer class confidence values, this approach is utilized for the classification task. To test the efficacy of the proposed classification model we used the Wisconsin Breast Cancer Dataset (WBCD). The performance is evaluated using classification accuracy, sensitivity, specificity, F-measure, area under curve, and Kappa statistics. The obtained classification accuracy of 99.7151% is a very promising result compared to the existing works in this area reporting the results for the same data set.