3-Satisfiability Reverse Analysis Method for Breast Cancer Detection

Accurate breast cancer screening is essential to ensure patient with such symptom can be treated accordingly. Medical screening is quite complicated since every patient sign and symptoms will be screened and when the number of features increases the medical practitioner will not able to be screened appropriately. 3Satisfiability Reverse Analysis Method (3-SATRA) incorporated with Hopfield neural network is a new approach for the early detection in breast cancer medical dataset. 3-SATRA has proposed to extract the best logic rule that will representing the attribute of breast cancer dataset since the conventional data extraction techniques focus only on standalone neural network. The proposed method is applied to Breast Cancer dataset obtained from UCI machine learning repository. To pursue that, the results of the analysis will promote the early detection stage used for medical practitioners. The simulation will be executed using Dev C++ 5.11 as a tool for training, testing and validating the performances of the proposed method. The performance of the method was measured based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sum of Squared Error (SSE), and Computational Time. The performance and accuracy of the results obtained have shown the effectiveness of 3SATRA in medical data mining.