• DOI:10.4108/EAI.24-4-2019.2284088
• Corpus ID: 181621730

The Hybrid Machine Learning Model Based on Random Forest Optimized by PSO and ACO for Predicting Heart Disease

@article{Khourdifi2019TheHM,
  title={The Hybrid Machine Learning Model Based on Random Forest Optimized by PSO and ACO for Predicting Heart Disease},
  author={Youness Khourdifi and Mohamed Bahaj},
  journal={Proceedings of the Third International Conference on Computing and Wireless Communication Systems, ICCWCS 2019, April 24-25, 2019, Faculty of Sciences, Ibn Tofa{\"i}l University -K{\'e}nitra- Morocco},
  year={2019}
}

• Y. Khourdifi, M. Bahaj
• Published 23 May 2019
• Computer Science
• Proceedings of the Third International Conference on Computing and Wireless Communication Systems, ICCWCS 2019, April 24-25, 2019, Faculty of Sciences, Ibn Tofaïl University -Kénitra- Morocco

In this paper, we used the hybrid Machine Learning model, for proposed PA-RF, a classification based on Random Forest model, optimized by Particle Swarm Optimization (PSO) associated with Ant Colony Optimization (ACO), and we use Fast Correlation-Based Feature Selection (FCBF) method to filter redundant and irrelevant characteristics, in order to improve the quality of heart disease classification. The proposed mixed approach is applied to the heart disease dataset. The results demonstrate the effectiveness and robustness of the proposed hybrid method in processing various types of data for the classification of heart disease. Therefore, this study examines the different automatic learning algorithms and compares the results using different performance measures, i.e. Accuracy, Precision, Recall, F1-Score, etc. The data set used in this study comes from the UCI's automatic learning repository, entitled "Heart Disease" Data set. We can be concluded that PA-RF has demonstrated efficiency and robustness compared to other classification methods.