General Information
    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
    • Managing Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • Average Days from Submission to Acceptance: 192 days
    • E-mail:
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Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2022 Vol.14(3): 97-103 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2022.V14.1316

Prediction of Cardiovascular Disease Using Feature Selection Techniques

Priya Singh, Gyanendra Kumar Pal, and Sanjeev Gangwar

Abstract—Cardiovascular diseases 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 diseases 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 diseases 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 systems. With these results, it is found that Logistic Regression achieved the highest accuracy of 84% as compared to the others.

Index Terms—Cardiovascular diseases (CVDs), Chi-square, data mining, logistic regression, Naive Bayes’, Pearson correlation, random forest, Support Vector Machine (SVM).

Priya Singh, Gyanendra Kumar Pal, and Sanjeev Gangwar are with VBS Purvanchal University, India (e-mail:,


Cite:Priya Singh, Gyanendra Kumar Pal, and Sanjeev Gangwar, "Prediction of Cardiovascular Disease Using Feature Selection Techniques," International Journal of Computer Theory and Engineering vol. 14, no. 3, pp. 97-103, 2022.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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