Prediction of heart disease is very important because it is one of the causes of death around the world. Moreover, heart disease prediction in the early stage plays a main role in the treatment and recovery disease and reduces costs of diagnosis disease and side effects it. Machine learning algorithms are able to identify an effective pattern for diagnosis and treatment of the disease and identify effective factors in the disease. this paper is investigated a new hybrid algorithm of Whale Optimization and Dragonfly algorithm using a machine learning algorithm. the hybrid algorithm employs a Support Vector Machine algorithm for effective Prediction of heart disease. Proposed method is evaluated by Cleveland standard heart disease dataset. The experimental result indicates that the SVM accuracy of 88.89 $\%$ and nine features are selected in this respect.
Eskandari, M., & Hassani, Z. (2019). Intelligent application for Heart disease detection using Hybrid Optimization algorithm. Journal of Algorithms and Computation, 51(1), 15-27. doi: 10.22059/jac.2019.71277
MLA
Marzieh Eskandari; Zeinab Hassani. "Intelligent application for Heart disease detection using Hybrid Optimization algorithm". Journal of Algorithms and Computation, 51, 1, 2019, 15-27. doi: 10.22059/jac.2019.71277
HARVARD
Eskandari, M., Hassani, Z. (2019). 'Intelligent application for Heart disease detection using Hybrid Optimization algorithm', Journal of Algorithms and Computation, 51(1), pp. 15-27. doi: 10.22059/jac.2019.71277
VANCOUVER
Eskandari, M., Hassani, Z. Intelligent application for Heart disease detection using Hybrid Optimization algorithm. Journal of Algorithms and Computation, 2019; 51(1): 15-27. doi: 10.22059/jac.2019.71277
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