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Predicting Cancer Relapse with Clinical Data: A Survey of Current Techniques.

, and . IRI, page 369-376. IEEE Computer Society, (2016)

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Building and Interpreting Risk Models from Imbalanced Clinical Data., and . ICTAI, page 143-150. IEEE, (2018)Melanoma risk modeling from limited positive samples., and . Netw. Model. Anal. Health Informatics Bioinform., 8 (1): 7 (2019)A survey of open source tools for machine learning with big data in the Hadoop ecosystem., , , and . J. Big Data, (2015)Approximating Learning Curves for Imbalanced Big Data with Limited Labels., and . ICTAI, page 237-242. IEEE, (2019)A review of statistical and machine learning methods for modeling cancer risk using structured clinical data., and . Artif. Intell. Medicine, (2018)Predicting Cancer Relapse with Clinical Data: A Survey of Current Techniques., and . IRI, page 369-376. IEEE Computer Society, (2016)Efficient learning from big data for cancer risk modeling: A case study with melanoma., and . Comput. Biol. Medicine, (2019)Sample size determination for biomedical big data with limited labels., and . Netw. Model. Anal. Health Informatics Bioinform., 9 (1): 12 (2020)Predicting Melanoma Risk from Electronic Health Records with Machine Learning Techniques.. Florida Atlantic University, Boca Raton, USA, (2019)ndltd.org (oai:fau.digital.flvc.org:fau_41962).Learning Curve Estimation with Large Imbalanced Datasets., and . ICMLA, page 763-768. IEEE, (2019)