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

Abstract

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.

Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes

“…21 Furthermore, ANNs have been shown to perform well on datasets of varying size. [22][23][24] Our results support the use of an ANN in a moderate sized, highdimensional dataset, whilst having a non-inferior performance pro le to a Cox regression model. The Cox regression model used 11 predictors to calculate survival function, whilst the ANN uses all 21 input features, and attributes different weightings to each feature.…”

Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes

“…24 Furthermore, ANNs have been shown to perform well on datasets of varying size. [25][26][27] Our results support the use of an ANN in a moderate sized, highdimensional dataset, whilst having a non-inferior performance pro le to a Cox regression model. The Cox regression model used 11 predictors to calculate survival function, whilst the ANN uses all 21 input features, and attributes different weightings to each feature.…”

Identifying the Machine Learning Techniques for Classification of Target Datasets

“…Secondly, LR: a standard statistical approach that is ideal for performing regression analysis where the dependent variable is binary. It is used to describe the data and to explain the relationship between one dependent binary variable with one or more independent nominal, ordinal, interval, or ratio-level variables [9]. Thirdly, the NB classifier: combines the Bayes paradigm with the decision rules like the hypothesis, which provides satisfactory results.…”