  Background and Objective: Breast cancer, which accounts for 23% of all
cancers, is threatening the communities of developing countries because of poor
awareness and treatment. Early diagnosis helps a lot in the treatment of the
disease. The present study conducted in order to improve the prediction process
and extract the main causes impacted the breast cancer. Materials and Methods:
Data were collected based on eight attributes for 130 Libyan women in the
clinical stages infected with this disease. Data mining was used by applying
six algorithms to predict disease based on clinical stages. All the algorithms
gain high accuracy, but the decision tree provides the highest accuracy-diagram
of decision tree utilized to build rules from each leafnode. Ranking variables
applied to extract significant variables and support final rules to predict
disease. Results: All applied algorithms were gained a high prediction with
different accuracies. Rules 1, 3, 4, 5 and 9 provided a pure subset to be
confirmed as significant rules. Only five input variables contributed to
building rules, but not all variables have a significant impact. Conclusion:
Tumor size plays a vital role in constructing all rules with a significant
impact. Variables of inheritance, breast side and menopausal status have an
insignificant impact in analysis, but they may consider remarkable findings
using a different strategy of data analysis.
