Robust breast cancer prediction system based on rough set theory at National Cancer Institute of Egypt. (January 2018) - Hamouda, Saeed Khodary M.; Wahed, Mohammed E.; Abo Alez, Reda. H.; Riad, Khaled
Highlights: This is one of the first studies using Rough Set theory for early prediction and diagnosis of breast cancer according to the High Risk Status attribute. Our approach has been compared with the clinical, RBF, and ANN classifications. A novel robust breast cancer prediction and diagnosis system based on the Rough Set (RS) has been proposed. Abstract: Background: Breast cancer is one of the major death causing diseases of the women in the world. Every year more than million women are diagnosed with breast cancer more than half of them will die because of inaccuracies and delays in diagnosis of the disease. High accuracy in cancer prediction is important to improve the treatment quality and the survivability rate of patients. Objectives: In this paper, we are going to propose a new and robust breast cancer prediction and diagnosis system based on the Rough Set (RS). Also, introducing the robust classification process based on some new and most effective attributes. Comparing and evaluating the performance of our proposed approach with the clinical, Radial Basis Function, and Artificial Neural Networks classification schemes. Methods : The dataset used in our experiments consists of 60 samples obtained from the National Cancer Institute (NCI) of Egypt. We have used the RS theory to robustly find dependence relationships among data, and evaluate the importance of attributes through: Applying the Approximation Sets on this dataset to identify the patient's cancer stage (0, IA, IB, IIA, IIB, IIIA, IIIB, IIIC, IV); and Running the Reduction process on this dataset to identify which attributes (symptoms) are most effective for description and predict breast cancer stage. Results: Our approach has classified the patients into 9 different stages, Stage 0 with accuracy 75%, Stage IA with accuracy 71%, Stage IB with zero patients, Stage IIA with accuracy 86%, Stage IIB with accuracy 67.5%, Stage IIIA with accuracy 85%, Stage IIIB with accuracy 100%, Stage IIIC with zero patients, and Stage IV with accuracy 100%; The Reduction process gives as output, the most effective symptoms to early predict and accurately diagnosis the breast cancer. That are represented in Lymph Node Status, Tumor Size, Estrogen Receptor Status, Progesterone Receptor Status, and Metastasis; and The last but not least, we have found two patients (patient No. 11 and 51 from our dataset) in High Risk Status, which requires intensive and special treatment. Conclusion: We have introduced the robustness of the RS theory in early predicting and diagnosing the breast cancer. This lay more importance to the contribution and efficiency of RS theory in the field of computational biology.