Prediction Axillary Lymph Node Involvement Status on Breast Cancer Data

Introduction: one of the foremost usual methods for evaluating breast cancer is the removal of axillary lymph nodes (ALN) which include complications such as edema, limited hand movements, and lymph accumulation. Although studies have shown that the sentinel gland condition represents the axillary nodules context in the mammary gland, the efficacy, and safety of the guard node biopsy need to be evaluated. Subsequently, predicting axillary lymph node status before sentinel lymph node biopsy needs regular clinical data collection and would be supportive for oncologists and could keep the clinicians away from this strategy. Predictive modeling for lymph node statues may be one way to diminish the axillary lymph node dissection (ALND) and consequences. Methods: The database used in this study was provided by Clinical Research Department, Breast Cancer Research Center, Motamed Cancer Institute (ACECR), Tehran, Iran. It contains clinical and demographic risk factors records of 5142 breast cancer patients from which a total of 38 features were selected. We performed modeling; based on six data mining algorithms (Decision Tree, Nave Bayesian, Random Forest, Support Vector Machine, Fast Large Margin, and Gradient Boosted Tree (GBT)). For evaluating the model, we used 10-fold cross-validation in Rapid Miner v9.7.001. Results: The results showed that the GBT model has a higher ability to predict lymph node metastasis than other models with an receiver operating characteristic (ROC) of 97%, a sensitivity of 96.59%, an accuracy of 90%, and specificity of 81% Conclusions: Obviously, we have to diagnose cancer with a needle biopsy before surgery. Used data mining predictions and use of them to create a clinical decision support system for predicting cancer and lymph node statuses can help physicians and pathologists make the best decision for a patient s ALN surgery