Comparative Study of Heart Failure Prediction Algorithm: Logistic Regression and SVM

Authors

  • Anang Prasetyo Computer Science Deapartment, School of Computer Science, Bina Nusantara University, Jakarta Indonesia 11480
  • Erick Computer Science Deapartment, School of Computer Science, Bina Nusantara University, Jakarta Indonesia 11480
  • Diva Angelika Mulia Computer Science Deapartment, School of Computer Science, Bina Nusantara University, Jakarta Indonesia 11480
  • Keiko Kimberly Octavina Computer Science Deapartment, School of Computer Science, Bina Nusantara University, Jakarta Indonesia 11480

Keywords:

Machine Learning, Heart Disease, Support Vector Machine, Logistic Regression

Abstract

Heart and blood vessel function is the biggest causes of death globally. Some researchers used machine learning approaches to predict it earlier and effective. There is a various machine learning to predict heart disease including Logistic Regression and Support Vector Machine. Many researchers using some of dataset, the dataset used is a dataset of heart disease patients obtained from the UCI Machine Learning Repository. This study comparing the performance of Logistic Regression and Support Victor Machine to predict it. Finally, the results Logistic Regression and SVM has average same performance, the AUC Support Vector Machine value is around 93.58% and Logistic Regression is around 92.95%, the SVM value is 0.63% higher than Logistic Regression.

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Published

17.02.2023

How to Cite

[1]
A. . Prasetyo, Erick, D. . Angelika Mulia, and K. . Kimberly Octavina, “Comparative Study of Heart Failure Prediction Algorithm: Logistic Regression and SVM”, Int J Intell Syst Appl Eng, vol. 11, no. 2, pp. 518–522, Feb. 2023.

Issue

Vol. 11 No. 2 (2023): (Article in Press)

Section

Research Article

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