A Comparative Analysis of C4.5 Classification Algorithm, Naïve Bayes and Support Vector Machine Based on Particle Swarm Optimization (PSO) for Heart Disease Prediction

A Comparative Analysis of C4.5 Classification Algorithm, Naïve Bayes and Support Vector Machine Based on Particle Swarm Optimization (PSO) for Heart Disease Prediction

• By Sidauruk, Juniato;
• Jurnal Internasional
• 380
• 84-95

Heart disease is a general term for all of types of the disorders which is affects

the heart. This research aims to compare several classification algorithms

known as the C4.5 algorithm, Naïve Bayes, and Support Vector Machine. The

algorithm is about to optimize of the heart disease predicting by applying

Particle Swarm Optimization (PSO). Based on the test results, the accuracy

value of the C4.5 algorithm is about 74.12% and Naïve Bayes algorithm

accuracy value is about 85.26% and the last the Support Vector Machine

algorithm is about 85.26%. From the three of algorithms above then continue

to do an optimization by using Particle Swarm Optimization. The data is

shown that Naïve Bayes algorithm with Particle Swarm Optimization has the

highest value based on accuracy value of 86.30%, AUC of 0.895 and precision

of 87.01%, while the highest recall value is Support Vector Machine algorithm

with Particle Swarm Optimization of 96.00%. Based on the results of the

research has been done, the algorithm is expected can be applied as an

alternative for problem solving, especially in predicting of the heart disease.

• Katakunci International Journal of Advances in Data and Information Systems
• URL http://ijadis.org
• Penerbit ,

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