DOI: 10.1101/472555Nov 17, 2018Paper

Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database

BioRxiv : the Preprint Server for Biology
Sandip PanesarJuan Fernandez-Miranda

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 histopathological 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 glioma patients. Methods We applied three machine learning techniques: artificial neural networks (ANN), decision trees (DT), support vector machine (SVM), and classical logistic regression (LR) to a dataset consisting of 76 glioma patients 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/AUC): 70.7%/0.70 for ANN, 68%/0.72 for SVM, 66.7%/0.64 for LR and 65%/0.70 for DT. Feature selected input consis...Continue Reading

Related Concepts

Malignant Neoplasms
Classification
Genetic Vectors
Glioma
Learning
Literature
Regression Analysis
Trees (plant)
World Health Organization
Logistic Regression

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