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

Dedi Saputra; Weishky Steven Dharmawan; Windi Irmayani

doi:10.25008/ijadis.v3i2.1243

Authors

Dedi Saputra UNIVERSITAS BINA SARANA INFORMATIKA KAMPUS KOTA PONTIANAK
Weishky Steven Dharmawan Information System, Universitas Bina Sarana Informatika
Windi Irmayani Accounting Information System, Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.25008/ijadis.v3i2.1243

Keywords:

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

Abstract

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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