ArticleAsia-Pacific journal of oncology nursing2022
Comparison of machine learning models for predicting the risk of breast cancer-related lymphedema in Chinese women.
Article in Asia-Pacific journal of oncology nursing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 17 citations in OpenAlex.
- Merging artificial intelligence into cancer nursing care: Current applications, challenges, and opportunities.Asia-Pacific journal of oncology nursing · 2026Article
- Using Machine Learning to Predict Breast Cancer-Related Lymphedema Following Axillary Lymph Node Dissection.Annals of surgical oncology · 2026Article
- A dermal backflow atlas of lymphatic dysfunction in breast cancer-related lymphedema with machine learning-based stage prediction.Breast cancer research and treatment · 2026Article
- AI Trustworthiness in the Perioperative Period for Patients with Serious Illness: Scoping Narrative Review.JMIR perioperative medicine · 2026Article
- Artificial Intelligence for Prediction of Breast Cancer-Related Lymphedema: A Systematic Review.Plastic and reconstructive surgery. Global open · 2026Article
- Artificial Intelligence In The Diagnosis And Prediction Of Breast Cancer-Related Lymphedema: A Scoping Review.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Technology-enhanced compression and AI-integrated lymphedema care: a narrative review.Irish journal of medical science · 2025Review
- Artificial Intelligence in Breast Cancer Diagnosis and Surgical Decision-Making: An Updated and Comprehensive Overview of Precision and Personalization in Current Evidence.Cancer management and research · 2025Review
- Machine learning-based personalized risk prediction model for breast cancer-related lymphedema after surgery.Frontiers in oncology · 2025Article
- Predictive modeling of breast cancer-related lymphedema using machine learning algorithms.Gland surgery · 2024Article
- Comparison of machine learning models to predict the risk of breast cancer-related lymphedema among breast cancer survivors: a cross-sectional study in China.Frontiers in oncology · 2024Article
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Predictive models for the occurrence of cancer symptoms by using machine learning (ML) algorithms could be used to aid clinical decision-making in order to enhance the quality of cancer care. This study aimed to develop and validate a selection of classification models that used ML algorithms to predict the occurrence of breast cancer-related lymphedema (BCRL) among Chinese women. Methods: This was a retrospective cohort study of consecutive cases that had been diagnosed with breast cancer, stages I-IV. Forty-eight variables were grouped into five feature sets. Five classification models with ML algorithms were developed, and the models' performance and the variables' relative importance were assessed accordingly. Results: Of 370 eligible female participants, 91 had BCRL (24.6%). The mean age of this study sample was 49.89 (SD = 7.45). All participants had had breast cancer surgery, and more than half of them had had a modified radical mastectomy ( Conclusions: This study found that in the ML training dataset, the multilayer perceptron model and the logistic regression model were the best discrimination models for predicting the outcome of BCRL, and the
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