Main Article Content
Decision tree, Multilayer neural network, Breast cancer, Data analysis
Methods: A data set from Motamed Cancer Institute's breast cancer research clinic, Tehran, containing 2860 records related to breast cancer risk factors were used. Of the records, 1141 (40%) were related to malignant changes and breast cancer and 1719 (60%) to benign tumors. The data set was analyzed using perceptron neural network and decision tree algorithms, and was split into two a training data set (70%) and a testing data set (30%) using Rapid Miner 5.2.
Results: For neural networks, accuracy was 80.52%, precision 88.91%, and sensitivity 90.88%; and for decision tree, accuracy was 80.98%, precision 80.97%, and sensitivity 89.32%. Results indicated that both algorithms have acceptable capabilities for analyzing breast cancer data.
Conclusion: Although both models provided good results, neural network showed more reliable diagnosis for positive cases. Data set type and analysis method affect results. On the other hand, information about more powerful risk factors of breast cancer, such as genetic mutations, can provide models with high coverage.