الفهرس 
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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.