Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes
Abstract
Artificial neural networks are algorithms that can be used to perform nonlinear statistical modeling and provide a new alternative to logistic regression, the most commonly used method for developing predictive models for dichotomous outcomes in medicine. Neural networks offer a number of advantages, including requiring less formal statistical training, ability to implicitly detect complex nonlinear relationships between dependent and independent variables, ability to detect all possible interactions between predictor variables, and the availability of multiple training algorithms. Disadvantages include its "black box" nature, greater computational burden, proneness to overfitting, and the empirical nature of model development. An overview of the features of neural networks and logistic regression is presented, and the advantages and disadvantages of using this modeling technique are discussed.
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DL models consistently outperform both regression-based and other ML models.
“Our results indicated that DL models consistently outperform both regression-based and other ML models, aligning with findings from previous studies (71, 72).”
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“Our results indicated that DL models consistently outperform both regression-based and other ML models, aligning with fi ndings from previous studies (71, 72).”
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- Authors
- Tu, J. V.
- Affiliations
- Sunnybrook Health Science Centre (CA)
- Corresponding author
- Tu, Jack V.
- ORCID
- Tu, Jack V.
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 1996-11
- Volume
- 49
- Issue
- 11
- Pages
- 1225-1231
- ISSN
- 0895-4356 (ISSN-L)
- OpenAlex
- W2121394390
- Cited by
- 1,818
- References
- 1
- Citations by year
- 2026: 39 · 2025: 81 · 2024: 130 · 2023: 147 · 2022: 159 · 2021: 189 · 2020: 126 · 2019: 141 · 2018: 125 · 2017: 119 · 2016: 92 · 2015: 74 · 2014: 57 · 2013: 55 · 2012: 46
- FWCI
- 17.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Clinical Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- artificial neural networks, logistic regression, binary outcomes, nonlinear models, predictor interactions, overfitting, black-box models, computational load
- MeSH terms
- Algorithms, Humans, Prognosis, Logistic Models, Neural Networks, Computer, Treatment Outcome
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