Optimization of Feature Selection in Support Vector Machines (SVM) Using Recursive Feature Elimination (RFE) and Particle Swarm Optimization (PSO) for Heart Disease Detection-Reference-Cited by-同舟云学术

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Optimization of Feature Selection in Support Vector Machines (SVM) Using Recursive Feature Elimination (RFE) and Particle Swarm Optimization (PSO) for Heart Disease Detection

Published:2024-08-13 Issue: Volume:19 Page:304-309
ISSN:
Container-title:2024 9th International Conference on Mechatronics Engineering (ICOM)
language:
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Author:

Bayuaji Luhur¹,Kusnadi ²,Amzah Mochamad Yamin²,Pebrianti Dwi³

Affiliation:

1. INTI International University,Faculty of Data Science & Information Technology,Nilai,Malaysia

2. Universitas Budi Luhur,Faculty of Information Technology,Jakarta,Indonesia

3. Kulliyyah of Engineering, International Islamic University Malaysia,Department of Mechanical & Aerospace Engineering,Gombak,Malaysia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10651580/10652257/10652561.pdf?arnumber=10652561

Reference14 articles.

1. The 30 Years of Shifting in The Indonesian Cardiovascular Burden—Analysis of The Global Burden of Disease Study

2. Technologies for non-invasive physiological sensing: Status, challenges, and future horizons

3. Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System

4. Heart Disease Detection Using Machine Learning;Chithambaram;Res Sq,2020

5. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning;Dinh;BMC Med Inform Decis Mak,2019

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