Enhancing Heart Disease Prediction Using Ensemble Techniques

Authors

  • Wasilah Sada Department of Computer Science, National Mathematical Centre Abuja, Nigeria
  • Celinus Kiyea Department of Computer Science Ahmadu Bello University Zaria, Nigeria.
  • Baththama Bello Alhassan Iya Abubakar Institute of ICT, Ahmadu Bello University, Zaria, Nigeria
  • Aminat Bolatito Yusuf Department of ICT, Usmanu Danfodiyo University, Sokoto, Nigeria
  • Fatsuma Jauro Department of Computer Science Ahmadu Bello University Zaria, Nigeria.
  • Maria Abur Iya Abubakar Institute of ICT, Ahmadu Bello University, Zaria, Nigeria

DOI:

https://doi.org/10.56471/slujst.v4i1&2.277

Keywords:

— Heart Diseases, feature selection, ensemble technique, prediction

Abstract

Cardiovascular diseases are recognized generally to be among the number one illness causing death across the globe. Predicting heart disease using a computer-aided technique makes it easier for medical practitioners to diagnose and thereby saving lives and reducing costs. Feature selection has become an essential component for developing Machine learning models. 

References

Abdollahi, J., & Nouri-Moghaddam, B. (2021). Feature selection for medical diagnosis: Evaluation for using a hybrid Stacked-Genetic approach in the diagnosis of heart disease. 11.

Published

2022-07-20

Issue

Vol. 4 No. 1&2 (2022): Special Issue on Computing & Advances in Information Technology

Section

Articles