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

Machine Learning (1465) Clinical Information and Decision Making (1407) Breast Cancer (206) Clinical Informatics (1022) Decision Support for Health Professionals (1185)

Published on in Vol 10, No 2 (2022): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33440, first published .
The Application and Comparison of Machine Learning Models for the Prediction of Breast Cancer Prognosis: Retrospective Cohort Study

The Application and Comparison of Machine Learning Models for the Prediction of Breast Cancer Prognosis: Retrospective Cohort Study

The Application and Comparison of Machine Learning Models for the Prediction of Breast Cancer Prognosis: Retrospective Cohort Study

Authors of this article:

Jialong Xiao1, 2, 3 Author Orcid Image ;   Miao Mo2, 3 Author Orcid Image ;   Zezhou Wang2, 3 Author Orcid Image ;   Changming Zhou2, 3 Author Orcid Image ;   Jie Shen2, 3 Author Orcid Image ;   Jing Yuan2, 3 Author Orcid Image ;   Yulian He1, 2 Author Orcid Image ;   Ying Zheng2, 3, 4 Author Orcid Image
Article Authors Cited by (21) Tweetations (8) Metrics

Journals

  1. Wu R, Luo J, Wan H, Zhang H, Yuan Y, Hu H, Feng J, Wen J, Wang Y, Li J, Liang Q, Gan F, Zhang G, Gupta D. Evaluation of machine learning algorithms for the prognosis of breast cancer from the Surveillance, Epidemiology, and End Results database. PLOS ONE 2023;18(1):e0280340 View
  2. Kebede S, Sebastian Y, Yeneneh A, Chanie A, Melaku M, Walle A. Prediction of contraceptive discontinuation among reproductive-age women in Ethiopia using Ethiopian Demographic and Health Survey 2016 Dataset: A Machine Learning Approach. BMC Medical Informatics and Decision Making 2023;23(1) View
  3. Shanbehzadeh M, Kazemi-Arpanahi H, Bolbolian Ghalibaf M, Orooji A. Performance evaluation of machine learning for breast cancer diagnosis: A case study. Informatics in Medicine Unlocked 2022;31:101009 View
  4. Hassan M, Hassan M, Yasmin F, Khan M, Zaman S, Galibuzzaman , Islam K, Bairagi A. A comparative assessment of machine learning algorithms with the Least Absolute Shrinkage and Selection Operator for breast cancer detection and prediction. Decision Analytics Journal 2023;7:100245 View
  5. Wang Y, Deng Y, Tan Y, Zhou M, Jiang Y, Liu B. A comparison of random survival forest and Cox regression for prediction of mortality in patients with hemorrhagic stroke. BMC Medical Informatics and Decision Making 2023;23(1) View
  6. Nguyen Q, Nguyen P, Wang C, Phuc P, Lin R, Hung C, Kuo N, Cheng Y, Lin S, Hsieh Z, Cheng C, Hsu M, Hsu J. Machine learning approaches for predicting 5‐year breast cancer survival: A multicenter study. Cancer Science 2023;114(10):4063 View
  7. Yang X, Qiu H, Wang L, Wang X. Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study. Journal of Medical Internet Research 2023;25:e44417 View
  8. Wang N, Lin Y, Song H, Huang W, Huang J, Shen L, Chen F, Liu F, Wang J, Qiu Y, Shi B, Lin L, He B. Development and validation of a model for the prediction of disease-specific survival in patients with oral squamous cell carcinoma: based on random survival forest analysis. European Archives of Oto-Rhino-Laryngology 2023;280(11):5049 View
  9. Tizi W, Berrado A. Machine learning for survival analysis in cancer research: A comparative study. Scientific African 2023;21:e01880 View
  10. Thakur N, Cui S, Patel K, Azizi N, Knieling V, Han C, Poon A, Shah R. Marburg Virus Outbreak and a New Conspiracy Theory: Findings from a Comprehensive Analysis and Forecasting of Web Behavior. Computation 2023;11(11):234 View
  11. Jhang J, Yu W, Huang W, Kuo H. Combination of urinary biomarkers and machine-learning models provided a higher predictive accuracy to predict long-term treatment outcomes of patients with interstitial cystitis/bladder pain syndrome. World Journal of Urology 2024;42(1) View
  12. Divya P, Suresh S. Bioinformatics Analysis in the Identification of Prognostic Signatures for ER-Negative Breast Cancer Data. Journal of the Indian Society for Probability and Statistics 2024;25(1):1 View
  13. Zhao H, Zhu C, Lian Y, Cheng Y, Zhu F, Wang J, Zheng Q. Identifying Factors Affecting the Survival of Patients with HIV-Associated B-Cell Lymphoma Using a Random Survival Forest Model. Clinical Medicine Insights: Oncology 2024;18 View
  14. Xing X, Li L, Sun M, Yang J, Zhu X, Peng F, Du J, Feng Y. Deep-learning-based 3D super-resolution CT radiomics model: Predict the possibility of the micropapillary/solid component of lung adenocarcinoma. Heliyon 2024;10(13):e34163 View
  15. Ong W, Lee A, Tan W, Fong K, Lai D, Tan Y, Low X, Ge S, Makmur A, Ong S, Ting Y, Tan J, Kumar N, Hallinan J. Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging—A Systematic Review. Cancers 2024;16(17):2988 View
  16. Zhou H, Fang C, Pan Y. Development of a System for Predicting Hospitalization Time for Patients With Traumatic Brain Injury Based on Machine Learning Algorithms: User-Centered Design Case Study. JMIR Human Factors 2024;11:e62866 View
  17. Abubakari S, Karaman F. Neutral Benchmarking of Survival Models in Health Sciences: Comparative Study of Classical and Machine Learning Techniques. Gazi University Journal of Science Part A: Engineering and Innovation 2024;11(3):518 View

Books/Policy Documents

  1. Naveen Venkatesh S, Sugumaran V, Divya S. Artificial Intelligence and Machine Learning for Women’s Health Issues. View
  2. Schimitz de Carvalho D, Capriles P, Goliatt L. Intelligent Systems Design and Applications. View
  3. Suresh S, Divya P, Ramadurai M. Deep Sciences for Computing and Communications. View