ArticleTuberculosis and respiratory diseases2023
Prediction of Postoperative Lung Function in Lung Cancer Patients Using Machine Learning Models.
Article in Tuberculosis and respiratory diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Predicting 5-Year Mortality in Non-Small-Cell Lung Cancer Using the Korean Central Cancer Registry: Model Development and Validation Study.JMIR medical informatics · 2026Article
- A Novel Machine Learning Approach to Assist Early Diagnosis of Diffuse Panbronchiolitis.Journal of Korean medical science · 2025Article
- Machine learning-based estimation of patient body weight from radiation dose metrics in computed tomography.Journal of applied clinical medical physics · 2024Article
- The role of the diaphragm in prediction of respiratory function in the immediate postoperative period in lung cancer patients using a machine learning model.World journal of surgical oncology · 2023Article
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundSurgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models.
methodsWe extracted data from the Clinical Data Warehouse and developed three sets: set I, the linear regression model; set II, machine learning models omitting the missing data: and set III, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), Ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in 1 second measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set III. Predictive performance was evaluated by R2 and mean squared error (MSE) in the three sets.
resultsA total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R2 value was 0.27 and in set II, LightGBM was the best model with the highest R2 value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R2 value of 0.56 and the lowest MSE of 174.07.
conclusionThe LightGBM model showed the best performance in predicting postoperative lung function.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.