ArticleFrontiers in genetics2019
Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Breast Cancer Survivors.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 46 citations in OpenAlex.
- Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
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- Using machine learning to identify the most important predictors of fatty liver index in healthy young Taiwanese men.Scientific reports · 2026Article
- The Comparison of Insulin Resistance Between Normal and Early Menopause Women Younger than Fifty Years Old by Machine Learning Methods.Diagnostics (Basel, Switzerland) · 2025Article
- Machine learning to evaluate the effects of non-clinical social determinant features in predicting colorectal Cancer mortality in a medically underserved Appalachian population.Scientific reports · 2025Article
- Artificial Intelligence in Cancer Diagnosis: A Game-changer in Healthcare.Current pharmaceutical biotechnology · 2025Review
- Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer Patients.Journal of imaging · 2024Article
- The risk factors determined by four machine learning methods for the change of difference of bone mineral density in post-menopausal women after three years follow-up.Scientific reports · 2024Article
- Longitudinal Risk Analysis of Second Primary Cancer after Curative Treatment in Patients with Rectal Cancer.Diagnostics (Basel, Switzerland) · 2024Article
- Using Machine Learning to Identify the Relationships between Demographic, Biochemical, and Lifestyle Parameters and Plasma Vitamin D Concentration in Healthy Premenopausal Chinese Women.Life (Basel, Switzerland) · 2023Article
- Comparison between multiple logistic regression and machine learning methods in prediction of abnormal thallium scans in type 2 diabetes.World journal of clinical cases · 2023Article
- Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort.Journal of the Chinese Medical Association : JCMA · 2023Article
- Roles of biochemistry data, lifestyle, and inflammation in identifying abnormal renal function in old Chinese.World journal of clinical cases · 2023Article
- Machine Learning Predictive Models for Evaluating Risk Factors Affecting Sperm Count: Predictions Based on Health Screening Indicators.Journal of clinical medicine · 2023Article
- Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran.International journal of environmental research and public health · 2023Article
- Assessing the length of hospital stay for patients with myasthenia gravis based on the data mining MARS approach.Frontiers in neurology · 2023Article
- Comparison between Machine Learning and Multiple Linear Regression to Identify Abnormal Thallium Myocardial Perfusion Scan in Chinese Type 2 Diabetes.Diagnostics (Basel, Switzerland) · 2022Article
- Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin-Creatinine Ratio in a 4-Year Follow-Up Study.Journal of clinical medicine · 2022Article
- Review
- Associations between Sex and Risk Factors for Predicting Chronic Kidney Disease.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to the high effectiveness of cancer screening and therapies, the diagnosis of second primary cancers (SPCs) has increased in women with breast cancer. The present study was conducted to develop a novel machine learning-based classification scheme for predicting the risk factors of SPCs in breast cancer survivors. The proposed scheme was based on the XGBoost classifier with the following four comparable strategies: transformation, resampling, clustering, and ensemble learning, to improve the training balanced accuracy. Results suggested that the best prediction accuracy for an empirical case is the XGBoost associated with the strategies of resampling and clustering. The experimental results showed that age, sequence of radiotherapy and surgery, surgical margins of the primary site, human epidermal growth factor, high-dose clinical target volume, and estrogen receptors are relatively more important risk factors associated with SPCs in patients with breast cancer. These risk factors should be monitored for the early detection of breast cancer. In conclusion, the proposed scheme can support the important influence of personality and clinical symptom representations in all phases of the primary treatment trajectory. Our results further suggested that adaptive machine learning techniques require the incorporation of significant variables for optimal predictions.
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