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

Machine Learning (1401) Clinical Information and Decision Making (1340) Decision Support for Health Professionals (1147) Breast Cancer (201)

Published on in Vol 8, No 6 (2020): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17364, first published .
Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development

Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development

Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development

Authors of this article:

Can Hou1 Author Orcid Image ;   Xiaorong Zhong2, 3 Author Orcid Image ;   Ping He2, 3 Author Orcid Image ;   Bin Xu1 Author Orcid Image ;   Sha Diao1 Author Orcid Image ;   Fang Yi1 Author Orcid Image ;   Hong Zheng2, 3 Author Orcid Image ;   Jiayuan Li1 Author Orcid Image
Article Authors Cited by (27) Tweetations (3) Metrics

Journals

  1. S V, A J, R S, Mohan S, Bhattacharya S, Kaluri R, Feng G, Tariq U. Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting. International Journal of Distributed Sensor Networks 2020;16(11):155014772097150 View
  2. Zhong X, Luo T, Deng L, Liu P, Hu K, Lu D, Zheng D, Luo C, Xie Y, Li J, He P, Pu T, Ye F, Bu H, Fu B, Zheng H. Multidimensional Machine Learning Personalized Prognostic Model in an Early Invasive Breast Cancer Population-Based Cohort in China: Algorithm Validation Study. JMIR Medical Informatics 2020;8(11):e19069 View
  3. Yun H, Park J, Choi D, Shin S, Jang M, Kong H, Kim S. Enhancement in Performance of Septic Shock Prediction Using National Early Warning Score, Initial Triage Information, and Machine Learning Analysis. The Journal of Emergency Medicine 2021;61(1):1 View
  4. Zhong T, Zhuang Z, Dong X, Wong K, Wong W, Wang J, He D, Liu S. Predicting Antituberculosis Drug–Induced Liver Injury Using an Interpretable Machine Learning Method: Model Development and Validation Study. JMIR Medical Informatics 2021;9(7):e29226 View
  5. Yun H, Choi J, Park J. Prediction of Critical Care Outcome for Adult Patients Presenting to Emergency Department Using Initial Triage Information: An XGBoost Algorithm Analysis. JMIR Medical Informatics 2021;9(9):e30770 View
  6. Lee Y, Choi J, Shin E. Machine learning model for diagnostic method prediction in parasitic disease using clinical information. Expert Systems with Applications 2021;185:115658 View
  7. Gopukumar D, Ghoshal A, Zhao H. Predicting Readmission Charges Billed by Hospitals: Machine Learning Approach. JMIR Medical Informatics 2022;10(8):e37578 View
  8. Lou S, Hou M, Chang H, Lee H, Chiu C, Yeh S, Shi H. Breast Cancer Surgery 10-Year Survival Prediction by Machine Learning: A Large Prospective Cohort Study. Biology 2021;11(1):47 View
  9. Senthilkumar B, Zodinpuii D, Pachuau L, Chenkual S, Zohmingthanga J, Kumar N, Hmingliana L. Ensemble Modelling for Early Breast Cancer Prediction from Diet and Lifestyle. IFAC-PapersOnLine 2022;55(1):429 View
  10. Zhao H, Zhang X, Xu Y, Gao L, Ma Z, Sun Y, Wang W. Predicting the Risk of Hypertension Based on Several Easy-to-Collect Risk Factors: A Machine Learning Method. Frontiers in Public Health 2021;9 View
  11. Góngora Alonso S, Marques G, Agarwal D, De la Torre Díez I, Franco-Martín M. Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia. Sensors 2022;22(7):2517 View
  12. Wang S, Liu H, Yang T, Huang M, Zheng B, Wu T, Han L, Zhang Y, Ren J. Machine learning based on automated breast volume scanner (ABVS) radiomics for differential diagnosis of benign and malignant BI‐RADS 4 lesions. International Journal of Imaging Systems and Technology 2022;32(5):1577 View
  13. Kourou K, Exarchos K, Papaloukas C, Sakaloglou P, Exarchos T, Fotiadis D. Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis. Computational and Structural Biotechnology Journal 2021;19:5546 View
  14. Istasy P, Lee W, Iansavichene A, Upshur R, Gyawali B, Burkell J, Sadikovic B, Lazo-Langner A, Chin-Yee B. The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review. Journal of Medical Internet Research 2022;24(11):e39748 View
  15. Hanis T, Ruhaiyem N, Arifin W, Haron J, Wan Abdul Rahman W, Abdullah R, Musa K. Over-the-Counter Breast Cancer Classification Using Machine Learning and Patient Registration Records. Diagnostics 2022;12(11):2826 View
  16. Chen M, Kong C, Qiao E, Chen Y, Chen W, Jiang X, Fang S, Zhang D, Chen M, Chen W, Ji J. Multi-algorithms analysis for pre-treatment prediction of response to transarterial chemoembolization in hepatocellular carcinoma on multiphase MRI. Insights into Imaging 2023;14(1) View
  17. Wang C, Guan X, Bai Y, Feng Y, Wei W, Li H, Li G, Meng H, Li M, Jie J, Fu M, Wu X, He M, Zhang X, Yang H, Lu Y, Guo H. A machine learning–based biological aging prediction and its associations with healthy lifestyles: the Dongfeng–Tongji cohort. Annals of the New York Academy of Sciences 2022;1507(1):108 View
  18. Al-Hammad W, Kuroda M, Kamizaki R, Tekiki N, Ishizaka H, Kuroda K, Sugimoto K, Oita M, Tanabe Y, Barham M, Sugianto I, Shimizu Y, Nakamitsu Y, Asaumi J. Mean Heart Dose Prediction Using Parameters of Single-Slice Computed Tomography and Body Mass Index: Machine Learning Approach for Radiotherapy of Left-Sided Breast Cancer of Asian Patients. Current Oncology 2023;30(8):7412 View
  19. Anastasi G, Franchini M, Pieroni S, Buzzi M, Buzzi M, Leporini B, Molinaro S. Machine learning techniques in breast cancer preventive diagnosis: a review. Multimedia Tools and Applications 2024 View
  20. Valencia-Moreno J, Gonzalez-Fraga J, Gutierrez-Lopez E, Estrada-Senti V, Cantero-Ronquillo H, Kober V. Breast cancer risk estimation with intelligent algorithms and risk factors for Cuban women. Computers in Biology and Medicine 2024;179:108818 View

Books/Policy Documents

  1. Singh S, Rajaraman R, Verlekar T. Data Management, Analytics and Innovation. View
  2. Panigrahi S, Swapnarekha H, Subudhi S. Nature-Inspired Optimization Methodologies in Biomedical and Healthcare. View