Computers in biology and medicine · 2025
The best machine learning algorithm for building surgical site infection predictive models: A systematic review and network meta-analysis.
СтатьяСистематический обзорNetwork Meta-AnalysisНазвание и аннотация: Перевод в очереди
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Shan Jiao, Bao Xiaoyuan, Wang Bin, Wang Yanbin, Wang Yan, Lv Meng, Huai Wei, Jin Yicheng, Jin Yixi, Zhang Zexin, Cao Yulong
Аннотация
BACKGROUND: Many machine learning (ML) algorithms have been used to develop surgical site infection (SSI) prediction models, but little is known about their predicting performance. We conducted a network meta-analysis to compare the performance of different ML algorithms and to explore which one may perform best.
METHODS: MEDLINE, EMBASE, CINAHL, Web of Science, and Cochrane Library were systematically searched from inception to November 25, 2023. We included diagnostic accuracy trials constructing SSI predictive model by ML. Two reviewers selected relevant studies and extracted data. The certainty of the evidence was rated using the QUADAS-2 tool. Performance statistics of the diagnostic analysis and the ranking of the different ML algorithms have been expressed in Relative Diagnostic Odds Ratio (RDOR) and superiority index (SI), respectively, using statistical software STATA and R.
RESULTS: Of 493 articles identified, 10 algorithms from 84 SSI prediction models in 40 articles were included in this review. The results of our study revealed that models based on solely surgical type outperformed models without discrimination of surgical type (RDOR 2.71, 95 % CI: 1.25-5.90, P = 0.01), and mixed-use of structured and textual data-based models outperformed models solely based on structured data (RDOR 8.70, 95 % CI: 3.65-20.75, P < 0.01). Combining the ML algorithms in different databased subgroups separately yields the sorted results: Boosted Classifiers had the best overall prediction for the mixed databased model (SI6.17, 95 % CI: 0.09, 13.00), and Support Vector Machine for the structured (SI 4.70, 95 % CI: 0.11, 13.00).
CONCLUSIONS: ML algorithms developed with structured and textual data provided optimal performance. Boosted Classifiers may be the best algorithm in SSI prediction.
METHODS: MEDLINE, EMBASE, CINAHL, Web of Science, and Cochrane Library were systematically searched from inception to November 25, 2023. We included diagnostic accuracy trials constructing SSI predictive model by ML. Two reviewers selected relevant studies and extracted data. The certainty of the evidence was rated using the QUADAS-2 tool. Performance statistics of the diagnostic analysis and the ranking of the different ML algorithms have been expressed in Relative Diagnostic Odds Ratio (RDOR) and superiority index (SI), respectively, using statistical software STATA and R.
RESULTS: Of 493 articles identified, 10 algorithms from 84 SSI prediction models in 40 articles were included in this review. The results of our study revealed that models based on solely surgical type outperformed models without discrimination of surgical type (RDOR 2.71, 95 % CI: 1.25-5.90, P = 0.01), and mixed-use of structured and textual data-based models outperformed models solely based on structured data (RDOR 8.70, 95 % CI: 3.65-20.75, P < 0.01). Combining the ML algorithms in different databased subgroups separately yields the sorted results: Boosted Classifiers had the best overall prediction for the mixed databased model (SI6.17, 95 % CI: 0.09, 13.00), and Support Vector Machine for the structured (SI 4.70, 95 % CI: 0.11, 13.00).
CONCLUSIONS: ML algorithms developed with structured and textual data provided optimal performance. Boosted Classifiers may be the best algorithm in SSI prediction.