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

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...

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Bibliographic Details
Published in:Scientific reports 2020-07, Vol.10 (1), p.10693, Article 10693
Main Authors: Sim, Jin-ah, Kim, Young Ae, Kim, Ju Han, Lee, Jong Mog, Kim, Moon Soo, Shim, Young Mog, Zo, Jae Ill, Yun, Young Ho
Format: Article
Language:English
Subjects:
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Aged
Algorithms
Cancer Survivors - psychology
Female
Humanities and Social Sciences
Humans
Learning algorithms
Lung cancer
Lung diseases
Lung Neoplasms - psychology
Machine Learning
Male
Middle Aged
multidisciplinary
Patient Reported Outcome Measures
Prediction models
Prognosis
Quality of life
Quality of Life - psychology
Risk Factors
Science
Science (multidisciplinary)
Surgery
Survival
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Summary: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 fivefold cross-validation. For feature set 1, there were no significant differences in model accuracies (ranging from 0.647 to 0.713). Among the models in feature set 2, the AdaBoost and RF models outperformed the other prognostic models [area under the curve (AUC) = 0.850, 0.898, 0.981, 0.966, and 0.949 for the DT, LR, bagging, RF and AdaBoost models, respectively] in the test set. Overall, 5-year disease-free lung cancer survival prediction models with MLTs that included HRQOL as well as clinical variables improved predictive performance.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-67604-3