Heart disease detection using Hybrid Whale Optimization Algorithm and Dragonfly algorithm

Heart disease is leading causes of death around the world and diagnosis of disease has high costs and severe side effects. Prediction of heart disease play an important role in the treatment and recovery. Moreover, heart disease prediction in the early stage ameliorate the process of rehabilitation. In this paper, a new hybrid algorithm is introduced which is a combination of a Whale Optimization Algorithm and Dragonfly algorithm and can be applied for feature selection. When it comes to classification, the hybrid algorithm employs Support Vector Machine algorithm. Proposed method is evaluated by Cleveland standard heart disease dataset. The experimental result represents that the classification accuracy of 88.89 % and nine features are selected in this respect.