classification-learner package (MathWorks Inc)
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90
Structured Review
MathWorks Inc
classification-learner package
Classification Learner Package, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/classification-learner+package/pmc11843972-61-27-27
Average 90 stars, based on 1 article reviews
Classification Learner Package, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/classification-learner+package/pmc11843972-61-27-27
Average 90 stars, based on 1 article reviews
classification-learner package - by Bioz Stars,
2026-09
90/100 stars
Images
1) Product Images from "Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review"
Article Title: Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review
Journal: BMC Medical Research Methodology
doi: 10.1186/s12874-025-02463-y
Figure Legend Snippet: Characteristics, classification and objectives of the machine learning models used in the studies
Techniques Used: Software, Biomarker Discovery, Diagnostic Assay, Imaging, Staining
Related Articles
Software:Article Title: Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images Article Snippet: These algorithms were chosen given their widespread use in classification studies and easy-to-use implementations in the Article Title: Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review Article Snippet: Varghese [ ] b , Incidence risk stratification (classification): QSVM (radiomics-based) , Risk stratification for prostate cancer in low- and high-risk patients , desktop-based , Article Title: Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images Article Snippet: This study can be expanded and improved upon by employing larger cohorts for developing and validating the risk stratifier, testing classification algorithms beyond those available in Matlab’s classification-learner package and qualitatively interpreting the stratifier to inform radiomics, clinical imaging and possibly even PCa biology. Biomarker Discovery:Article Title: Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images Article Snippet: These algorithms were chosen given their widespread use in classification studies and easy-to-use implementations in the Article Title: Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review Article Snippet: Varghese [ ] b , Incidence risk stratification (classification): QSVM (radiomics-based) , Risk stratification for prostate cancer in low- and high-risk patients , desktop-based , Article Title: Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images Article Snippet: This study can be expanded and improved upon by employing larger cohorts for developing and validating the risk stratifier, testing classification algorithms beyond those available in Matlab’s classification-learner package and qualitatively interpreting the stratifier to inform radiomics, clinical imaging and possibly even PCa biology. Diagnostic Assay:Article Title: Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images Article Snippet: These algorithms were chosen given their widespread use in classification studies and easy-to-use implementations in the Article Title: Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review Article Snippet: Varghese [ ] b , Incidence risk stratification (classification): QSVM (radiomics-based) , Risk stratification for prostate cancer in low- and high-risk patients , desktop-based , Article Title: Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images Article Snippet: This study can be expanded and improved upon by employing larger cohorts for developing and validating the risk stratifier, testing classification algorithms beyond those available in Matlab’s classification-learner package and qualitatively interpreting the stratifier to inform radiomics, clinical imaging and possibly even PCa biology. Imaging:Article Title: Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images Article Snippet: These algorithms were chosen given their widespread use in classification studies and easy-to-use implementations in the Article Title: Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review Article Snippet: Varghese [ ] b , Incidence risk stratification (classification): QSVM (radiomics-based) , Risk stratification for prostate cancer in low- and high-risk patients , desktop-based , Article Title: Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images Article Snippet: This study can be expanded and improved upon by employing larger cohorts for developing and validating the risk stratifier, testing classification algorithms beyond those available in Matlab’s classification-learner package and qualitatively interpreting the stratifier to inform radiomics, clinical imaging and possibly even PCa biology. Staining:Article Title: Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images Article Snippet: These algorithms were chosen given their widespread use in classification studies and easy-to-use implementations in the Article Title: Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review Article Snippet: Varghese [ ] b , Incidence risk stratification (classification): QSVM (radiomics-based) , Risk stratification for prostate cancer in low- and high-risk patients , desktop-based , Article Title: Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images Article Snippet: This study can be expanded and improved upon by employing larger cohorts for developing and validating the risk stratifier, testing classification algorithms beyond those available in Matlab’s classification-learner package and qualitatively interpreting the stratifier to inform radiomics, clinical imaging and possibly even PCa biology. Comparison:Article Title: Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images Article Snippet: These algorithms were chosen given their widespread use in classification studies and easy-to-use implementations in the Article Title: Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review Article Snippet: Varghese [ ] b , Incidence risk stratification (classification): QSVM (radiomics-based) , Risk stratification for prostate cancer in low- and high-risk patients , desktop-based , Article Title: Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images Article Snippet: This study can be expanded and improved upon by employing larger cohorts for developing and validating the risk stratifier, testing classification algorithms beyond those available in Matlab’s classification-learner package and qualitatively interpreting the stratifier to inform radiomics, clinical imaging and possibly even PCa biology. |