ArticlePloS one2017
SVM and SVM Ensembles in Breast Cancer Prediction.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 1 of them a synthesis that pooled it.
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Who cites it
76 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning prediction in cardiovascular diseases: a meta-analysis.Scientific reports · 2020Pooled it
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- Personalized predictions of neoadjuvant chemotherapy response in breast cancer using machine learning and full-field digital mammography radiomics.Frontiers in medicine · 2025Article
- SRC is a potential target of Arctigenin in treating triple-negative breast cancer: based on machine learning algorithms, molecular modeling andFrontiers in molecular biosciences · 2025Article
- Identification of novel diagnostic biomarkers associated with liver metastasis in colon adenocarcinoma by machine learning.Discover oncology · 2024Article
- Reconstruction ofCurrent issues in molecular biology · 2024Article
- Exploring the potential of machine learning in gynecological care: a review.Archives of gynecology and obstetrics · 2024Review
- Smart Biosensor for Breast Cancer Survival Prediction Based on Multi-View Multi-Way Graph Learning.Sensors (Basel, Switzerland) · 2024Article
- Innovative approach towards early prediction of ovarian cancer: Machine learning- enabled XAI techniques.Heliyon · 2024Article
- Consumer electronics based smart technologies for enhanced terahertz healthcare having an integration of split learning with medical imaging.Scientific reports · 2024Article
- Machine learning models for predicting the onset of chronic kidney disease after surgery in patients with renal cell carcinoma.BMC medical informatics and decision making · 2024Article
- AI Deployment on GBM Diagnosis: A Novel Approach to Analyze Histopathological Images Using Image Feature-Based Analysis.Cancers · 2023Article
- Personalized Risk Assessment of Hepatic Fibrosis after Cholecystectomy in Metabolic-Associated Steatotic Liver Disease: A Machine Learning Approach.Journal of clinical medicine · 2023Article
- Prognostic prediction of left ventricular myocardial noncompaction using machine learning and cardiac magnetic resonance radiomics.Quantitative imaging in medicine and surgery · 2023Article
- Machine-learning predictive model of pregnancy-induced hypertension in the first trimester.Hypertension research : official journal of the Japanese Society of Hypertension · 2023Article
16 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.
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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.