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Comparison of machine learning and logistic regression models in predicting acute kidney injury: A systematic review and meta-analysis
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- TRV-2026-2770raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Systematic review or meta-analysis
- Sources
- 1 sourcePeer-reviewed
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INTRODUCTION: We aimed to assess whether machine learning models are superior at predicting acute kidney injury (AKI) compared to logistic regression (LR), a conventional prediction model. METHODS: Eligible studies were identified using PubMed and Embase. A total of 24 studies consisting of 84 prediction models met inclusion criteria. Independent samples t-test was performed to detect mean differences in area under the curve (AUC) between ML and LR models. One-way ANOVA and post-hoc t-tests were performed to ass…
The problem
A systematic review and meta-analysis of 24 studies (84 models) found machine learning predicted acute kidney injury no better than logistic regression, with average AUC of 0.736 versus 0.748.
What this doesn’t fix
Performance varied widely between studies and models, and most significant predictors were unique and study specific, so the pooled averages do not show how any single model would perform in a given hospital.
Sources
1 on file- International Journal of Medical Informaticsdoi:10.1016/j.ijmedinf.2021.104484
Cite this record
v1Truvace Impact Record TRV-2026-2770, v1: “Comparison of machine learning and logistic regression models in predicting acute kidney injury: A systematic review and meta-analysis.” Truvace, 2026-11-12. /record/TRV-2026-2770 (accessed on the day you copy it). sha256 1ecbb8966ebc1afd…
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