3-satisfiability logic programming approach for cardiovascular diseases diagnosis

3-Satisfiability logic programming is a brand-new approach in the data mining field. In recent years, the conventional data mining technique only emphasizes on the standalone neural network paradigm. To frame the novelty, the 3-Satisfiability logic programming is incorporated with the discrete Hopfield neural network as a single data mining tool. Hence, the proposed approach is applied in evaluating numerous cardiovascular diseases data sets. Pursuing that, the results obtained can assist the medical practitioners in cardiovascular disease early diagnosis. Dev C++ 5.11 was used as a platform for training, testing and validating the performances of the proposed approach. The performance of the proposed approach is evaluated by performance metrics such as root mean square error (RMSE), mean bias error (MBE), sum of squared error (SSE), symmetric mean absolute percentage error (SMAPE) and CPU time. The error evaluations and accuracy of the proposed method have demonstrated a promising result when applied in heart disease data set (HNN-3SATHDD) and statlog heart data (HNN-3SATSD). Therefore, the results had provided the concrete evidence of the effectiveness of the proposed approach in the data mining.