Iranian Association of Medical InformaticsFrontiers in Health Informatics2676-710410120210725Performance Analysis of Data Mining Techniques for the Prediction Breast Cancer Risk on Big Data29629610.30699/fhi.v10i1.296ENSolmazSohrabeiDepartment of Medical Informatics, Shahid Beheshti University of Medical Sciences, Tehran,. firstname.lastname@example.orgAlirezaAtashi202104212021071120210528Background: Comprehension of the components causing malignancy, event, and improvement of the infection as a reason for intercession for anticipation and fix. anticipating bosom malignant growth, which thusly will give experiences into the counteraction and fix of the infection. This clarifies the meaning of investigating the elements causing bosom malignancy. Methods: The database contains 7834 records of breast cancer patients' clinical and risk factors data. There were 4008 patients (52.4%) with breast cancers (malignant) and the remaining 3617 patients (47.6%) without breast cancers (benign). SVM, MLP, DT, KNN, RL, NB models were developed using 20 fields (risk factor) of the database. The present study divided the data into 10 folds where 1 fold for testing and 9 folds for training as a way of validating the 10-fold crossover validation. Ultimately, the comparison of the models was made based on sensitivity, specificity, and accuracy indicators.Results: In this study, Six different data mining classification techniques (NB, K-NN, SVM, C5, ANN, RL) were used for the prediction of breast cancer risk and their performance was compared in order to evaluate the best classifier. NB and ANN are better models for the prediction of breast cancer risks for the values of accuracy, Specificity, Sensitivity for Six models.Conclusion: Strangely the different AI calculations utilized in this examination yielded close precision subsequently these techniques could be utilized as option prescient instruments in the bosom malignancy Risk considers. The significant prognostic components affecting the Risk pace of bosom disease distinguished in this investigation, which were approved by Risk, are helpful and could be converted into choice help devices in the clinical area.