The prediction of heart disease is one of the areas where machine learning can be implemented. Optimization
algorithms have the advantage of dealing with complex non-linear problems with a good flexibility and
adaptability. In this paper, we exploited the Fast Correlation-Based Feature Selection (FCBF) method to filter
redundant features in order to improve the quality of heart disease classification. Then, we perform a
classification based on different classification algorithms such as K-Nearest Neighbour, Support Vector
Machine, Naïve Bayes, Random Forest and a Multilayer Perception | Artificial Neural Network optimized by
Particle Swarm Optimization (PSO) combined with Ant Colony Optimization (ACO) approaches. The proposed
mixed approach is applied to heart disease dataset; the results demonstrate the efficacy and robustness of
the proposed hybrid method in processing various types of data for heart disease classification. Therefore,
this study examines the different machine learning algorithms and compares the results using different
performance measures, i.e. accuracy, precision, recall, f1-score, etc. A maximum classification accuracy of
99.65% using the optimized model proposed by FCBF, PSO and ACO. The results show that the performance
of the proposed system is superior to that of the classification technique presented above.
Machine Learning based Energy Management at Internet of Things Network Nodes
