ArticlePloS one2020
Modeling and comparing data mining algorithms for prediction of recurrence of breast cancer.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Machine learning for early prediction of secondary cancer after radiotherapy.Scientific reports · 2025Article
- Predicting cancer mortality using machine learning methods: a global vs. Iran analysis.BMC cancer · 2025Article
- Article
- A Systematic Review of the Outcomes of Utilization of Artificial Intelligence Within the Healthcare Systems of the Middle East: A Thematic Analysis of Findings.Health science reports · 2024Review
- Predicting Breast Cancer Relapse from Histopathological Images with Ensemble Machine Learning Models.Current oncology (Toronto, Ont.) · 2024Article
- Utilizing patient data: A tutorial on predicting second cancer with machine learning models.Cancer medicine · 2024Article
- Prediction of subsequent fragility fractures: application of machine learning.BMC musculoskeletal disorders · 2024Article
- Use of artificial intelligence in breast surgery: a narrative review.Gland surgery · 2024Review
- Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form.PloS one · 2024Article
- Public health implications of computer-aided diagnosis and treatment technologies in breast cancer care.AIMS public health · 2023Review
- Comparing outcomes of single-port insufflation endoscopic breast-conserving surgery and conventional open approach for breast cancer.World journal of surgical oncology · 2022Article
- Application of Artificial Intelligence Techniques to Predict Risk of Recurrence of Breast Cancer: A Systematic Review.Journal of personalized medicine · 2022Review
- Prediction of lymphedema occurrence in patients with breast cancer using the optimized combination of ensemble learning algorithm and feature selection.BMC medical informatics and decision making · 2022Article
- A Comparative Evaluation of Cancer Classification via TP53 Gene Mutations Using Machin Learning.Asian Pacific journal of cancer prevention : APJCP · 2022Article
- Prediction of Breast Cancer using Machine Learning Approaches.Journal of biomedical physics & engineering · 2022Article
- Machine Learning Algorithms: Prediction and Feature Selection for Clinical Refracture after Surgically Treated Fragility Fracture.Journal of clinical medicine · 2022Article
- Breast cancer recurrence prediction with ensemble methods and cost-sensitive learning.Open medicine (Warsaw, Poland) · 2021Article
- Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study.Cancers · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the most common invasive cancer and the second leading cause of cancer death in women. and regrettably, this rate is increasing every year. One of the aspects of all cancers, including breast cancer, is the recurrence of the disease, which causes painful consequences to the patients. Moreover, the practical application of data mining in the field of breast cancer can help to provide some necessary information and knowledge required by physicians for accurate prediction of breast cancer recurrence and better decision-making. The main objective of this study is to compare different data mining algorithms to select the most accurate model for predicting breast cancer recurrence. This study is cross-sectional and data gathering of this research performed from June 2018 to June 2019 from the official statistics of Ministry of Health and Medical Education and the Iran Cancer Research Center for patients with breast cancer who had been followed for a minimum of 5 years from February 2014 to April 2019, including 5471 independent records. After initial pre-processing in dataset and variables, seven new and conventional data mining algorithms have been applied that each one represents one kind of data mining approach. Results show that the C5.0 algorithm possibly could be a helpful tool for the prediction of breast cancer recurrence at the stage of distant recurrence and nonrecurrence, especially in the first to third years. also, LN involvement rate, Her2 value, Tumor size, free or closed tumor margin were found to be the most important features in our dataset to predict breast cancer recurrence.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.