Data Mining Techniques in Predicting Breast Cancer

TL;DR

This study applies six data mining algorithms to predict breast cancer stages using clinical data, identifying tumor size as a key factor and demonstrating high prediction accuracy with decision trees.

Contribution

The paper compares multiple data mining techniques for breast cancer prediction and highlights the effectiveness of decision trees in extracting significant predictive rules.

Findings

Decision trees achieved the highest accuracy among algorithms.

Tumor size is a crucial predictor in breast cancer classification.

Inheritance, breast side, and menopausal status are less significant variables.

Abstract

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.