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

KHOURDIFI, Youness and BAHAJ, Mohamed (2019) The Hybrid Machine Learning Model Based on Random Forest Optimized by PSO and ACO for Predicting Heart Disease. In: ICCWCS 2019, 24-25 April 2019, Kenitra, Morocco.

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Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: machine learning; heart disease; random forest; ant colony optimization; particle swarm optimization
Subjects: T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 15 Oct 2021 07:07
Last Modified: 15 Oct 2021 07:07
URI: https://eprints.eudl.eu/id/eprint/7644

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