[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/3502060.3502156acmotherconferencesArticle/Chapter ViewAbstractPublication PagesbecbConference Proceedingsconference-collections
research-article

Performance Evaluation of Extreme Learning Machines Classification Algorithm for Medical Datasets

Published: 14 February 2022 Publication History

Abstract

The choice of efficient algorithms is a critical issue in the classification of medical datasets. This requires the consideration of a number of measures to ensure reliable results. In this study, the robustness of Extreme Learning Machine (ELM) and some state-of-arts classifiers were investigated on six (6) different (complete and incomplete) medical datasets. Multiple imputation technique with 5-fold-iteration was used to address the issue of missing data points in datasets with holes. The technique regenerated the missing values 100% in all the datasets. The performance of ELM was compared with Support Vector Machine (SVM), k-Nearest Neighbour (KNN) and Classification and Regression Tree (CART) on the complete and imputed datasets. The evaluations were based on classification accuracy, computational time and stability of the algorithms. ELM has 83.33% overall best accuracy, and 100% best computational time of the simulations. However, the stability of ELM is subject to further improvement, which is an area of further research.

References

[1]
P. López-Úbeda, M. C. Díaz-Galiano, T. Martín-Noguerol, A. Luna, L. A. Ureña-López, and M. T. Martín-Valdivia, 2021. “Automatic medical protocol classification using machine learning approaches,” Comput. Methods Programs Biomed., vol. 200.
[2]
N. Shahid, T. Rappon, and W. Berta, 2019. “Applications of artificial neural networks in health care organizational decision-making: A scoping review,” PLoS One, vol. 14, no. 2, pp. 1–22.
[3]
S. Song, M. Wang, and Y. Lin, 2020. “An improved algorithm for incremental extreme learning machine,” Syst. Sci. Control Eng., vol. 8, no. 1, pp. 308–317.
[4]
K. Mehrabani-Zeinabad, M. Doostfatemeh, and S. M. T. Ayatollahi, 2020. “An efficient and effective model to handle missing data in classification,” Biomed Res. Int., vol. 2020.
[5]
M. Qian and Y. F. Li, 2020. “Weakly supervised-based oversampling for high imbalance and high dimensionality data classification,” arXiv, pp. 1–9.
[6]
O. A. Alade, R. Sallehuddin, and A. Selamat, 2019. “Empirical Performance Evaluation of Imputation Techniques using Medical Dataset,” IOP Conf. Ser. Mater. Sci. Eng., vol. 551, no. 1.
[7]
F. Yang, 2020. “Missing Value Estimation Methods Research for Arrhythmia Classification Using the Modified Kernel Difference-Weighted KNN Algorithms,” Biomed Res. Int., vol. 2020.
[8]
W. C. Lin and C. F. Tsai, 2020. “Missing value imputation: a review and analysis of the literature (2006–2017),” Artif. Intell. Rev., vol. 53, no. 2, pp. 1487–1509.
[9]
O. A. Alade, R. Sallehuddin, N. H. M. Radzi, and A. Selamat, 2019. “Missing Data Characteristics and the Choice of Imputation Technique: An Empirical Study.,” in International Conference of Reliable Information and Communication Technology, 2019, pp. 88–97.
[10]
A. Rashno, B. Nazari, S. Sadri, and M. Saraee,2017. “Effective pixel classification of Mars images based on ant colony optimization feature selection and extreme learning machine,” Neurocomputing, vol. 226, no. November 2015, pp. 66–79.
[11]
R. Wang, J. Li, J. Wang, and C. Gao, 2018. “Research and application of a hybridwind energy forecasting system based on data processing and an optimized extreme learning machine,” Energies, vol. 11, no. 7.
[12]
R. Tang and X. Zhang, 2020. “CART Decision Tree Combined with Boruta Feature Selection for Medical Data Classification,” 2020 5th IEEE Int. Conf. Big Data Anal. ICBDA 2020, pp. 80–84.
[13]
D. B. Rubin, 1986. “Statistical matching using file concatenation with adjusted weights and multiple imputations,” J. Bus. Econ. Stat., vol. 4, no. 1, pp. 87–94.
[14]
M. Eshtay, H. Faris, and N. Obeid, 2018. “Improving Extreme Learning Machine by Competitive Swarm Optimization and its application for medical diagnosis problems,” Expert Syst. Appl., vol. 104, pp. 134–152.
[15]
H. Faris, S. Mirjalili, I. Aljarah, M. Mafarja, and A. A. Heidari, 2020. Nature-Inspired Optimizers, vol. 2020. Cham: Springer International Publishing.

Index Terms

  1. Performance Evaluation of Extreme Learning Machines Classification Algorithm for Medical Datasets
          Index terms have been assigned to the content through auto-classification.

          Recommendations

          Comments

          Please enable JavaScript to view thecomments powered by Disqus.

          Information & Contributors

          Information

          Published In

          cover image ACM Other conferences
          BECB 2021: 2021 International Symposium on Biomedical Engineering and Computational Biology
          August 2021
          262 pages
          ISBN:9781450384117
          DOI:10.1145/3502060
          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          Published: 14 February 2022

          Permissions

          Request permissions for this article.

          Check for updates

          Author Tags

          1. Computational cost
          2. imputation
          3. missing data
          4. normalization
          5. stability

          Qualifiers

          • Research-article
          • Research
          • Refereed limited

          Funding Sources

          Conference

          BECB 2021

          Contributors

          Other Metrics

          Bibliometrics & Citations

          Bibliometrics

          Article Metrics

          • 0
            Total Citations
          • 29
            Total Downloads
          • Downloads (Last 12 months)6
          • Downloads (Last 6 weeks)1
          Reflects downloads up to 09 Jan 2025

          Other Metrics

          Citations

          View Options

          Login options

          View options

          PDF

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          HTML Format

          View this article in HTML Format.

          HTML Format

          Media

          Figures

          Other

          Tables

          Share

          Share

          Share this Publication link

          Share on social media