3-satisfiability reverse analysis method with Hopfield neural network for medical data set

3-Satisfiability Reverse Analysis Method (3SATRA) incorporated with Hopfield neural network is a brand-new approach in extracting the logical rule in the form of 3-Satisfiability (3-SAT) to represent the behavior of a specific medical data set. The motivation of this research is to develop a robust hybrid algorithm to be applied in extracting the information and insights in medical data set. More specifically, 3SATRA is chosen in extracting the insights in term of logical rule from medical data sets. The 3SATRA approach will be combined with 3-SAT logic and HNN as a single data mining paradigm. The proposed method is employed to test and train the medical data set such as Breast Cancer Coimbra and Statlog Heart data set, generated from the standard UCI machine learning repository. The simulation is coded and executed using Dev C++ 5.11 by employing 60% training data and 40% of testing data. The performance of the method was measured based on standard performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sum of Squared Error (SSE), accuracy and CPU Time. The overall results show that the effectiveness of the proposed method in processing the medical data, in terms of lower RMSE, MAE, SSE and faster CPU Time. Additionally, the proposed models have achieved high and consistent accuracy after each of execution.