Improving Performance of KNN and C4.5 using Particle Swarm Optimization in Classification of Heart Diseases

Improving Performance of KNN and C4.5 using Particle Swarm Optimization in Classification of Heart Diseases

machine learning;classification;improving;performance;heart disease

Heart disease is a majorproblem that must be overcome for human life. In recent years, the volume of medical data related to heart disease has increased rapidly, and various heart disease data have collaborated with information technology such as machine learning in detecting, predicting,and classifying diseases.This research aimsto improve the performance of machine learning classification methods, namely K-Nearest Neighbor (KNN) and Decision Tree (C4.5) with the particle swarm optimization(PSO) feature in cases of heart disease. In this research, a comparison was made of the performance of the PSO-based K-NN and C4.5 algorithms. Following experiments employing PSO optimization to improvethe K-NN and C4.5 algorithms, the findings indicated that the K-NN algorithm performed exceptionally well with PSO, achieving an accuracy of 89.09%, precision of 89.61%, recall of 90.79%, and an AUC value of 0.935

Pareza Alam Jusia1, Abdul Rahim2, Herti Yani3, Jasmir Jasmir4

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5710/933

<span>Informatic Engineering Universitas Dinamika Bangsa, Jambi, Indonesia</span>

01-06-2024

FAJAR BAGUS W

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