Multidisciplinary Cancer Investigation

en Prediction Axillary Lymph Node Involvement Status on Breast Cancer Data Prevention, Early Detection and Screening Original/Research Article <div style="text-align: justify;">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.</div> Lymph Nodes, Axilla, Decision Support Systems, Clinical, Machine Learning, Breast Neoplasms 1 9 http://mcijournal.com/browse.php?a_code=A-10-180-1&slc_lang=en&sid=1 Solmaz Sohrabei solmazsohrabee1@gmail.com 10031947532846003314 10031947532846003314 Yes Department of Development, Management and Resources; Office of Statistic and Information Technology Management, Zanjan University of Medical Sci- ences, Zanjan, Iran Raheleh Salari 10031947532846003315 10031947532846003315 No Poostchi Ophthalmology Research Center, Department of Ophthalmology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran Seyed Mohammad Ayyoubzadeh 10031947532846003316 10031947532846003316 No Department of Health Information Management, School of Allied Medical Sci- ences, Tehran University of Medical Science, Tehran, Iran AlirezaAtashi Atashi 10031947532846003317 10031947532846003317 No Department of E-Health, Virtual School, Tehran University of Medical Sci- ences, Tehran, Iran & Medical Informatics Research Group, Clinical Research Department, Breast Cancer Research Center, Motamed Cancer Institute (ACECR), Tehran, Iran