An efficient hybrid extreme learning machine and evolutionary framework with applications for medical diagnosis

Authors/Creators

Ali Al Bataineh
Seyed M. J. Jalali, Edith Cowan UniversityFollow
Seyed J. Mousavirad
Amirmehdi Yazdani
Syed M. S. Islam, Edith Cowan UniversityFollow
Abbas Khosravi

Abstract

Integrating machine learning techniques into medical diagnostic systems holds great promise for enhancing disease identification and treatment. Among the various options for training such systems, the extreme learning machine (ELM) stands out due to its rapid learning capability and computational efficiency. However, the random selection of input weights and hidden neuron biases in the ELM can lead to suboptimal performance. To address this issue, our study introduces a novel approach called modified Harris hawks optimizer (MHHO) to optimize these parameters in ELM for medical classification tasks. By applying the MHHO-based method to seven medical datasets, our experimental results demonstrate its superiority over seven other evolutionary-based ELM trainer models. The findings strongly suggest that the MHHO approach can serve as a valuable tool for enhancing the performance of ELM in medical diagnosis.

Document Type

Journal Article

Date of Publication

4-1-2024

Volume

41

Issue

4

School

School of Science

Copyright

subscription content

Publisher

Wiley

Comments

Bataineh, A. A., Jalali, S. M. J., Mousavirad, S. J., Yazdani, A., Islam, S. M. S., & Khosravi, A. (2024). An efficient hybrid extreme learning machine and evolutionary framework with applications for medical diagnosis. Expert Systems, 41(4), article e13532. https://doi.org/10.1111/exsy.13532

Recommended Citation

Al Bataineh, A., Jalali, S. M., Mousavirad, S. J., Yazdani, A., Islam, S. M., & Khosravi, A. (2024). An efficient hybrid extreme learning machine and evolutionary framework with applications for medical diagnosis. DOI: https://doi.org/10.1111/exsy.13532