الفهرس | Only 14 pages are availabe for public view |
Abstract Breast cancer is a deadly disease in women. Predicting the breast cancer outcomes is very useful in determining the efficient treatment plan for the new breast cancer patients. Predicting the breast cancer outcomes (also called Prognosis) is done based on the previous patient’s data, which show the patient’s characteristics and how the doctors treated the patient. A new efficient model for predicting the main outcomes is proposed; Survival Rate, Disease Free Survival, and Recurrence detection; of breast cancer. The proposed model is called BCOAP ”Breast Cancer Outcome Accurate Predictor”, it’s utilizes two techniques to increase the accuracy of the predictive results. The first technique is applying the classification model on various data clusters rather than the full dataset. In such step the data is grouped in different clusters according the similarity of the main characteristics, then the classification model is applied on these clusters. The second technique is using the Hyper-Parameters Optimization (also called Hyper-Parameters Tuning) to increase the accuracy of the classification model. In this step the proposed model uses Hyper-Parameters Optimization to find a tuple of hyper-parameters that yields on the optimal model which minimizes a predefined loss function on given dataset. The results show the efficiency of the proposed BCOAP model in predicting the main outcomes ofthe breast cancer. The model achieved the highest prediction accuracy for the three main breast cancer outcomes; 5- Years survival rate (SR), breast cancer recurrence and disease free survival (DFS). The following section shows in details the results of the breast cancer main outcomes prediction. In the 5-years survival rate prediction, using the hyper- parameters optimization: clusters 3,4,5,7,8,9 accuracy’s have 9. |