The major effects of health-related quality of life on 5-year survival prediction among lung cancer survivors: applications of machine learning.

ABSTRACT: The primary goal of this study was to evaluate the major roles of health-related quality of life (HRQOL) in a 5-year lung cancer survival prediction model using machine learning techniques (MLTs). The predictive performances of the models were compared with data from 809 survivors who underwent lung cancer surgery. Each of the modeling technique was applied to two feature sets: feature set 1 included clinical and sociodemographic variables, and feature set 2 added HRQOL factors to the variables from feature set 1. One of each developed prediction model was trained with the decision tree (DT), logistic regression (LR), bagging, random forest (RF), and adaptive boosting (AdaBoost) methods, and then, the best algorithm for modeling was determined. The models' performances were compared using

SUBMITTER: Sim JA 

PROVIDER: S-EPMC7329866 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature