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http://bura.brunel.ac.uk/handle/2438/27034| Title: | A machine learning software tool for multiclass classification[Formula presented] |
| Authors: | Wang, S Lu, H Khan, A Hajati, F Khushi, M Uddin, S |
| Keywords: | disease comorbidity;disease multimorbidity;machine learning;multiclass classification |
| Issue Date: | 16-Jul-2022 |
| Publisher: | Elsevier |
| Citation: | Wang, S. et al. (2022) 'A machine learning software tool for multiclass classification[Formula presented]', Software Impacts, 13, 100383, pp. 1 - 4. doi: 10.1016/j.simpa.2022.100383. |
| Abstract: | Copyright © 2022 The Author(s). This paper describes code for a published article that can assist researchers with multiclass classification problems and analyse the performances of various machine learning models. Further, feature importance, feature correlation, variable clustering, confusion matrix and kernel density estimation were also implemented. The original study was published in Expert Systems with Applications, and this paper explains the code and workflow. Administrative healthcare data has been used as an example to run the code. The results and insights can assist healthcare stakeholders and policymakers reduce the negative impact of illness comorbidity and multimorbidity. |
| Description: | The code (and data) in this article has been certified as Reproducible by Code Ocean: (https://codeocean.com/). |
| URI: | https://bura.brunel.ac.uk/handle/2438/27034 |
| DOI: | https://doi.org/10.1016/j.simpa.2022.100383 |
| Other Identifiers: | ORCID iD: Matloob Khushi https://orcid.org/0000-0001-7792-2327 100383 |
| Appears in Collections: | Dept of Computer Science Research Papers |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | Copyright © 2022. Published by Elsevier B.V. This manuscript version is made available under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/). The version of record is available online at: https://doi.org/10.1016/j.simpa.2022.100383 (see: https://www.elsevier.com/about/policies/sharing). | 528 kB | Adobe PDF | View/Open |
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