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- 2019
Machine Learning Versus Logistic Regression Methods for 2-Year Mortality Prognostication in a Small, Heterogeneous Glioma DatabaseDOI: 10.1016/j.wnsx.2019.100012 Keywords: Diagnosis, Gliomas, Logistic regression, Machine learning, Neuro-oncology, Prognostication ANN, Artificial neural network, AUC, Area under the curve, CI, Confidence interval, DT, Decision tree, LR, Logistic regression, ML, Machine learning, NLR, Negative likelihood ratio, NPV, Negative predictive value, PLR, Positive likelihood ratio, PPV, Positive predictive value, SVM, Support vector machine, WHO, World Health Organization Abstract: Machine learning (ML) is the application of specialized algorithms to datasets for trend delineation, categorization, or prediction. ML techniques have been traditionally applied to large, highly dimensional databases. Gliomas are a heterogeneous group of primary brain tumors, traditionally graded using histopathologic features. Recently, the World Health Organization proposed a novel grading system for gliomas incorporating molecular characteristics. We aimed to study whether ML could achieve accurate prognostication of 2-year mortality in a small, highly dimensional database of patients with glioma
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