Machine Learning Versus Logistic Regression Methods for 2-Year Mortality Prognostication in a Small, Heterogeneous Glioma Database

Background 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. Methods We applied 3 ML techniques (artificial neural networks [ANNs], decision trees [DTs], and support vector machines [SVMs]) and classical logistic regression (LR) to a dataset consisting of 76 patients with glioma of all grades. We compared the effect of applying the algorithms to the raw database versus a database where only statistically significant features were included into the algorithmic inputs (feature selection). Results Raw input consisted of 21 variables and achieved performance of accuracy/area (C.I.) under the curve of 70.7%/0.70 (49.9–88.5) for ANN, 68%/0.72 (53.4–90.4) for SVM, 66.7%/0.64 (43.6–85.0) for LR, and 65%/0.70 (51.6–89.5) for DT. Feature selected input consisted of 14 variables and achieved performance of 73.4%/0.75 (62.9–87.9) for ANN, 73.3%/0.74 (62.1–87.4) for SVM, 69.3%/0.73 (60.0–85.8) for LR, and 65.2%/0.63 (49.1–76.9) for DT. Conclusions We demonstrate that these techniques can also be applied to small, highly dimensional datasets. Our ML techniques achieved reasonable performance compared with similar studies in the literature. Although local databases may be small versus larger cancer repositories, we demonstrate that ML techniques can still be applied to their analysis; however, traditional statistical methods are of similar benefit.