Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Dec 17, 2021
Date Accepted: Nov 25, 2022
Date Submitted to PubMed: Nov 25, 2022
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Assessment of performance of the machine learning-based breast cancer risk prediction model: a systematic review and meta-analysis
ABSTRACT
Background:
Background:
Machine learning algorithms well-suited in cancer research, especially in breast cancer for the investigation and development of riTo assess the performance of available machine learning-based breast cancer risk prediction model.
Objective:
Objective:
To assess the performance of available machine learning-based breast cancer risk prediction model.
Methods:
Methods:
As of June 9, 2021, articles on breast cancer risk prediction models by machine learning were searched in PubMed, Embase, and Web of Science. Studies describing the development or validation of risk prediction models for predicting future breast cancer risk were included. Pooled area under the curve (AUC) were calculated using the DerSimonian and Laird random-effects model.
Results:
Result: A total of 8 studies with 10 datasets were included. Neural network was the most common machine learning method for the development of risk prediction models. The pooled AUC of machine learning-based optimal risk prediction model reported in each study was 0.73 (95%CI: 0.66-0.80), which was higher than that of traditional risk factor-based risk prediction models (all Pheterogeneity < 0.001). The pooled AUC of neural network-based risk prediction model was higher than that of non-neural network-based optimal risk prediction model (0.71 vs. 0.68). Subgroup analysis showed that incorporation of imaging features risk models had a higher pooled AUC than model of non-incorporation of imaging features (0.73 vs. 0.61; Pheterogeneity =0.001).
Conclusions:
Conclusions:
The pooled machine learning-based breast cancer risk prediction model yield a good prediction performance and promising results.
Citation
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Copyright
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