Prediction of Cardiovascular Disease using Feature Selection Techniques

  • PRIYA SINGH UNSIET, VBS PURVANCHAL UNIVERSITY
  • Mr.Gyanendra Kumar Pal
  • Dr. Sanjeev Gangwar
Keywords: Chi-Square, Data Mining, Logistic Regression, Naive Bayes', Pearson correlation, Random Forest, Support Vector Machine(SVM)

Abstract

Cardiovascular disease is one of the harmful diseases and many people suffered from this disease across the world. In the field of the healthcare industry, on-time and efficient prediction of cardiovascular disease plays a prominent role in healthcare. Currently, the Medicare industry is “Data-rich" yet ”Insight poor”. The aim of this research work is to develop an efficient and accurate system to inspect Cardiovascular disease and the system is based on data mining techniques that can help to remedy such a situation. The system is developed based on classification algorithms like Random Forest, Logistic Regression, Naive Bayes’ and Support Vector Machine while feature selection algorithm has been used like Pearson Correlation and Chi-Square in order to increase the accuracy and reduce the execution time of classification system. With the results, it is found that Logistic Regression achieved the highest accuracy of 84% as compared to the others.

Published
2022-08-02
Section
Special Issue-The Advances in Comp. Theory & Software Eng. on Covid Pandemic(s)