ArticleScientific reports2020
The major effects of health-related quality of life on 5-year survival prediction among lung cancer survivors: applications of machine learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 4 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 4 syntheses or guidelines pooled it, 57 citations in OpenAlex.
- Machine Learning Techniques Used for the Identification of Sociodemographic Factors Associated With Cancer: Systematic Literature Review.Journal of medical Internet research · 2026Pooled it
- Machine learning (ML) techniques to predict breast cancer in imbalanced datasets: a systematic review.Journal of cancer survivorship : research and practice · 2025Pooled it
- Exploring the role of health-related quality of life measures in predictive modelling for oncology: a systematic review.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Pooled it
- Revolutionizing Lung Cancer Detection: A High-Accuracy Machine Learning Framework for Early Diagnosis.BioMed research international · 2025Pooled it
- Sing-a-Lung 2.0: protocol for a multicentre randomised controlled trial to investigate the effects and mechanisms of online-delivered singing training versus usual care 6-18 months after surgical resection for non-small cell lung cancer (NSCLC).BMJ open respiratory research · 2026Article
- Patient-reported outcomes after surgical resection for non-small cell lung cancer and perceptions of postsurgical rehabilitation programmes.BMJ open respiratory research · 2026Observational
- Causal Impact of Hemoglobin Levels on Global Quality of Life in Patients with Cancer.Journal of clinical medicine · 2026Article
- Quality of life during the COVID-19 pandemic and survival outcomes among breast cancer survivors.Breast cancer research and treatment · 2025Article
- Using artificial intelligence to predict patient outcomes from patient-reported outcome measures: a scoping review.Health and quality of life outcomes · 2025Article
- Enhanced Lung Cancer Survival Prediction Using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets.Cancers · 2025Article
- Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer Patients.Journal of imaging · 2024Article
- Exploring the potential of machine learning in gynecological care: a review.Archives of gynecology and obstetrics · 2024Review
- Using Machine Learning to Predict Unplanned Hospital Utilization and Chemotherapy Management From Patient-Reported Outcome Measures.JCO clinical cancer informatics · 2024Article
- Article
- Which are best for successful aging prediction? Bagging, boosting, or simple machine learning algorithms?Biomedical engineering online · 2023Article
- XML-GBM lung: An explainable machine learning-based application for the diagnosis of lung cancer.Journal of pathology informatics · 2023Article
- Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review.Asia-Pacific journal of oncology nursing · 2022Article
- The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review.Journal of medical Internet research · 2022Article
- Optimizing an existing prediction model for quality of life one-year post-intensive care unit: An exploratory analysis.Acta anaesthesiologica Scandinavica · 2022Article
- Prognostic value of patient-reported outcome measures (PROMs) in adults with non-small cell Lung Cancer: a scoping review.BMC cancer · 2022Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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 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.
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