Performance Comparison of the SVM and SVM-PSO Algorithms for Heart Disease Prediction


  • Weishky Steven Dharmawan Information System, Universitas Bina Sarana Informatika
  • Windi Irmayani Accounting Information System, Universitas Bina Sarana Informatika



Support Vector Machine, Particle Swarm Optimization, Classification, data mining, dataset, SVM, PSO


Data analysis for datasets with very large dimensions, classification is needed to predict from large datasets, in this study compare a method for classifying large data where the data will be processed to obtain the desired data prediction information. In this study, the Support Vector Machine (SVM) is used to provide the classification results of an algorithm that will be compared with the incorporation of the Support Vector Machine (SVM) and Particle Swarm Optimization (PSO) where the test results will be compared with the SVM classification algorithm only as a comparison algorithm. better at predicting than data sets. SVM is used as a single algorithm to see different experimental results when SVM is combined with PSO. From the experiments carried out, SVM got an Accuracy value of 81.85% and an AUC value of 0.823 while SVM-PSO got an Accuracy value of 84.81% and an AUC value of 0.898.


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How to Cite

Saputra, D., Dharmawan, W. S., & Irmayani, W. (2022). Performance Comparison of the SVM and SVM-PSO Algorithms for Heart Disease Prediction. International Journal of Advances in Data and Information Systems, 3(2), 74-86.
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