Issitt, Richard W;
Cortina-Borja, Mario;
Bryant, William;
Bowyer, Stuart;
Taylor, Andrew M;
Sebire, Neil;
(2022)
Classification Performance of Neural Networks Versus Logistic Regression Models: Evidence From Healthcare Practice.
Cureus
, 14
(2)
, Article e22443. 10.7759/cureus.22443.
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Abstract
Machine learning encompasses statistical approaches such as logistic regression (LR) through to more computationally complex models such as neural networks (NN). The aim of this study is to review current published evidence for performance from studies directly comparing logistic regression, and neural network classification approaches in medicine. A literature review was carried out to identify primary research studies which provided information regarding comparative area under the curve (AUC) values for the overall performance of both LR and NN for a defined clinical healthcare-related problem. Following an initial search, articles were reviewed to remove those that did not meet the criteria and performance metrics were extracted from the included articles. Teh initial search revealed 114 articles; 21 studies were included in the study. In 13/21 (62%) of cases, NN had a greater AUC compared to LR, but in most the difference was small and unlikely to be of clinical significance; (unweighted mean difference in AUC 0.03 (95% CI 0-0.06) in favour of NN versus LR. In the majority of cases examined across a range of clinical settings, LR models provide reasonable performance that is only marginally improved using more complex methods such as NN. In many circumstances, the use of a relatively simple LR model is likely to be adequate for real-world needs but in specific circumstances in which large amounts of data are available, and where even small increases in performance would provide significant management value, the application of advanced analytic tools such as NNs may be indicated.
| Type: | Article |
|---|---|
| Title: | Classification Performance of Neural Networks Versus Logistic Regression Models: Evidence From Healthcare Practice |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.7759/cureus.22443 |
| Publisher version: | https://doi.org/10.7759/cureus.22443 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Clinical informatics, electronic health records, logistic regression, machine learning, neural network, performance |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Childrens Cardiovascular Disease |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10146613 |
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