Predicting overall survival in chordoma patients using machine learning models: a web-app application.

ABSTRACT:

Objective

The goal of this study was to evaluate the efficacy of machine learning (ML) techniques in predicting survival for chordoma patients in comparison with the standard Cox proportional hazards (CoxPH) model.

Methods

Using a Surveillance, Epidemiology, and End Results database of consecutive newly diagnosed chordoma cases between January 2000 and December 2018, we created and validated three ML survival models as well as a traditional CoxPH model in this population-based cohort study. Randomly, the dataset was divided into training and validation datasets. Tuning hyperparameters on the training dataset involved a 1000-iteration random search with fivefold cross-validation. Concordance index (C-index), Brier score, and integrated Brier score were used to evaluate the pe

SUBMITTER: Cheng P 

PROVIDER: S-EPMC10474690 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature