A Novel Hybrid Approach for Classification Problem Case Study: Heart Disease Classification

DOI

10.2991/978-94-6463-094-7_32How to use a DOI?

Keywords

ELM-CSO; Extreme Learning Machine; Cuckoo Search Optimization; Heart Disease

Abstract

Heart disease is a major cause of death globally, with patients succumbing to death a few years of being diagnosed. This paper proposed a novel hybrid approach of Cuckoo Search Optimization – Extreme Learning Machine (CSO - ELM) to solve a classification problem. The approach was compared with established models proven in classifying heart disease. The CSO-ELM indicated significant predictive ability and outperformed the established and base models in machine learning.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ahmed Umer Khawaja
AU  - Yeh Ching Low
PY  - 2022
DA  - 2022/12/27
TI  - A Novel Hybrid Approach for Classification Problem Case Study: Heart Disease Classification
BT  - Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)
PB  - Atlantis Press
SP  - 413
EP  - 423
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-094-7_32
DO  - 10.2991/978-94-6463-094-7_32
ID  - Khawaja2022
ER  -