BFFA-NB: Hybrid Binary Farmland Fertility Algorithm with Naïve Bayes for Diagnosis of Heart Disease

One of the essential aims intelligent algorithms concerning diagnosis heart disease is to achieve accurate results and discover valuable patterns. This paper proposes a new hybrid model based on Binary Farmland Fertility Algorithm (BFFA) Naïve Bayes (NB) diagnose disease. The BFFA used for Feature Selection (FS) NB data classification. FS can be employed most beneficial features. Four valid universal UCI datasets (Heart, Cleveland, Hungary Switzerland) were Each dataset included 13 main evaluation proposed simulated in MATLAB 2017b. number features four Heart, Switzerland equal 13, which was reduced six each through better efficiency model. For evaluation, accuracy criterion, criterion all Switzerland, 82.25%, 86.91%, 89.32% 89.24%, respectively. Results showed appropriateness comparison some other methods. In this paper, compared with methods, it found out that possessed percentage.