High Accurate Prediction of Heart Disease Classification by Support Vector Machine

Titik Misriati, Riska Aryanti, Asriyani Sagiyanto

2023

Abstract

Heart disease is a prominent cause of mortality in developed and developing countries, including Indonesia. Conventional methods of diagnosing cardiac disease may not always be accurate, and there is an increasing demand for more modern and dependable procedures. The study aims to assess the effectiveness of several machine learning algorithms in heart disease categorization to determine the best effective algorithm for accurate diagnosis. Data mining techniques are one method for making predictions. This study employs decision tree algorithms, random forests, support vector machines, neural networks, and naive bayes to predict cardiac disease. Based on the results of the test shows that the accuracy of the Support Vector Machine algorithm is 81.97%, and the AUC 0.903 obtains higher accuracy than the Naı̈ve Bayes, Random Forest, Neural Network, and Decision Tree algorithms. Testing the Support Vector Machine algorithm using parameter C with values of 0.0, 1.0, 2.0, 3.0, 4.0, and 5.0 produces the best C parameter of 3.0 with an accuracy value of 85.25%. The results of this study, the Support Vector Machine algorithm, can be used for heart disease prediction because it has a high accuracy level and is included in the excellent classification in predicting heart disease.

Paper Citation

in Harvard Style

Misriati T., Aryanti R. and Sagiyanto A. (2023). High Accurate Prediction of Heart Disease Classification by Support Vector Machine. In Proceedings of the 3rd International Conference on Advanced Information Scientific Development - Volume 1: ICAISD; ISBN 978-989-758-678-1, SciTePress, pages 5-9. DOI: 10.5220/0012437100003848

in Bibtex Style

@conference{icaisd23,
author={Titik Misriati and Riska Aryanti and Asriyani Sagiyanto},
title={High Accurate Prediction of Heart Disease Classification by Support Vector Machine},
booktitle={Proceedings of the 3rd International Conference on Advanced Information Scientific Development - Volume 1: ICAISD},
year={2023},
pages={5-9},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012437100003848},
isbn={978-989-758-678-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Advanced Information Scientific Development - Volume 1: ICAISD
TI - High Accurate Prediction of Heart Disease Classification by Support Vector Machine
SN - 978-989-758-678-1
AU - Misriati T.
AU - Aryanti R.
AU - Sagiyanto A.
PY - 2023
SP - 5
EP - 9
DO - 10.5220/0012437100003848
PB - SciTePress