This paper is in the following e-collection/theme issue:

Innovative Methods in Public Health and Surveillance (383) Machine Learning (2076) Cancer Survivorship (334) Breast Cancer (290) Artificial Intelligence (2129) Cancer Prognosis Models and Machine Learning (67) Innovations and Technology in Cancer Research (43)

Published on in Vol 8, No 12 (2022): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35750, first published .
An Assessment of the Predictive Performance of Current Machine Learning–Based Breast Cancer Risk Prediction Models: Systematic Review

An Assessment of the Predictive Performance of Current Machine Learning–Based Breast Cancer Risk Prediction Models: Systematic Review

An Assessment of the Predictive Performance of Current Machine Learning–Based Breast Cancer Risk Prediction Models: Systematic Review

Authors of this article:

Ying Gao1 Author Orcid Image ;   Shu Li2 Author Orcid Image ;   Yujing Jin1 Author Orcid Image ;   Lengxiao Zhou1 Author Orcid Image ;   Shaomei Sun1 Author Orcid Image ;   Xiaoqian Xu1 Author Orcid Image ;   Shuqian Li1 Author Orcid Image ;   Hongxi Yang3 Author Orcid Image ;   Qing Zhang1 Author Orcid Image ;   Yaogang Wang4 Author Orcid Image
Article Authors Cited by (17) Tweetations (4) Metrics

Journals

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  6. Tang W, Mo S, Xie Y, Wei T, Chen G, Teng Y, Jia K. Predicting Overall Survival in Patients with Male Breast Cancer: Nomogram Development and External Validation Study. JMIR Cancer 2025;11:e54625 View
  7. Huang X, Ren S, Mao X, Chen S, Chen E, He Y, Jiang Y. Association Between Risk Factors and Major Cancers: Explainable Machine Learning Approach. JMIR Cancer 2025;11:e62833 View
  8. Rafiepoor H, Ghorbankhanloo A, Zendehdel K, Madar Z, Hajivalizadeh S, Hasani Z, Sarmadi A, Amanpour‐Gharaei B, Barati M, Saadat M, Sadegh‐Zadeh S, Amanpour S. Comparison of Machine Learning Models for Classification of Breast Cancer Risk Based on Clinical Data. Cancer Reports 2025;8(4) View
  9. Westerlinck P. Comparative Analysis of Predictive Models for Individual Cancer Risk: Approaches and Applications. Onco 2025;5(2):29 View
  10. Liu L, Zhou P, Hou L, Kao C, Zhang Z, Wang D, Yu L, Wang F, Wang Y, Yu Z. Development and performance of female breast cancer incidence risk prediction models: a systematic review and meta-analysis. Annals of Medicine 2025;57(1) View
  11. Garba A, Hamza H. Interpretable Machine Learning Approach for Breast Cancer Classification. Human-Centric Intelligent Systems 2025;5(3):308 View
  12. Wang Z, He X, Ou H, Li X, Wei C, Zhou R, Su Z, Mi J, Lu W, Wang F. An interpretable machine learning model for preoperative prediction of renal mass malignancy. Clinical and Translational Oncology 2025 View
  13. Xue J, Li Y, Qu T, Qin Y, Wang H, Rong X, Tian J, Wang T, Zhang J, Li Z, Ping Y. The clinical validity of radiomics-based prediction of molecular subtypes in breast cancer from digital mammary tomosynthesis. Frontiers in Oncology 2025;15 View
  14. Yao X, Ma A, Wang G, Ding Z, Zhu S, li D, Zhang H, Ding M, Shi S. Optimization of ultrasonic extraction and kinetic modeling of jieduquyuziyin prescription polysaccharides via explainable machine learning. Ultrasonics Sonochemistry 2025;122:107635 View
  15. Ho P, Loo C, Lim R, Goh M, Abubakar M, Ahearn T, Andrulis I, Antonenkova N, Aronson K, Augustinsson A, Behrens S, Bodelon C, Bogdanova N, Bolla M, Brantley K, Brenner H, Byers H, Camp N, Castelao J, Cessna M, Chang-Claude J, Chanock S, Chenevix-Trench G, Choi J, Colonna S, Czene K, Daly M, Derouane F, Dörk T, Eliassen A, Engel C, Eriksson M, Evans D, Fletcher O, Fritschi L, Gago-Dominguez M, Genkinger J, Geurts-Giele W, Glendon G, Hall P, Hamann U, Ho C, Ho W, Hooning M, Hoppe R, Howell A, Humphreys K, Ito H, Iwasaki M, Jakubowska A, Jernström H, John E, Johnson N, Kang D, Kim S, Kitahara C, Ko Y, Kraft P, Kwong A, Lambrechts D, Larsson S, Li S, Lindblom A, Linet M, Lissowska J, Lophatananon A, MacInnis R, Mannermaa A, Manoukian S, Margolin S, Matsuo K, Michailidou K, Milne R, Mohd Taib N, Muir K, Murphy R, Newman W, O'Brien K, Obi N, Olopade O, Panayiotidis M, Park S, Park-Simon T, Patel A, Peterlongo P, Plaseska-Karanfilska D, Pylkäs K, Rashid M, Rennert G, Rodriguez J, Saloustros E, Sandler D, Sawyer E, Scott C, Shahi S, Shu X, Shulman K, Simard J, Southey M, Stone J, Taylor J, Teo S, Teras L, Terry M, Torres D, Vachon C, Houdt M, Verhoeven J, Weinberg C, Wolk A, Yamaji T, Yip C, Zheng W, Hartman M, Li J. Threshold-Based Overlap of Breast Cancer High-Risk Classification Using Family History, Polygenic Risk Scores, and Traditional Risk Models in 180,398 Women. Cancers 2025;17(21):3561 View

Books/Policy Documents

  1. Yadav R, Rawat R, Jha S, Rahul M, Yadav V. Innovative Computing and Communications. View
  2. Almalki M, Nawaf L, Carroll F. Cybersecurity and Human Capabilities Through Symbiotic Artificial Intelligence. View