Performance Analysis of Rule Based Algorithms Applied to a Cardiovascular Dataset

Authors

  • Dev Mukherji B.Tech Computer Science & Engineering, VIT University, Vellore – 632014, TN
  • Nikita Padalia B.Tech Computer Science & Engineering, VIT University, Vellore – 632014, TN

DOI:

https://doi.org/10.24297/ijct.v12i2.3319

Keywords:

Heart disease, cardiovascular, rule based, support vector machines, logistic regression, decision trees, hybrid mining

Abstract

Cardiovascular disease is one of the dominant concerns of society, affecting millions of people each year. Early and accurate diagnosis of risk of heart disease is one of major areas of medical research, aimed to aid in its prevention and treatment. Most of the approaches used to predict the occurrence of heart disease use single data mining techniques. However, performances of predictive methods have recently increased upon research into hybrid and alternative methods. This paper analyses the performance of logistic regression, support vector machine, and decision trees along with rule-based hybrids of the three in an attempt to create a more accurate predictive model.

Downloads

Download data is not yet available.

Downloads

Published

2013-12-27

How to Cite

Mukherji, D., & Padalia, N. (2013). Performance Analysis of Rule Based Algorithms Applied to a Cardiovascular Dataset. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 12(2), 3277–3285. https://doi.org/10.24297/ijct.v12i2.3319

Issue

Section

Research Articles