Prediction of Postoperative Lung Function in Lung Cancer Patients Using Machine Learning Models

BACKGROUND: Surgical 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. METHODS: We 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 R(2) and mean squared error (MSE) in the three sets. RESULTS: A total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R(2) value was 0.27 and in set II, LightGBM was the best model with the highest R(2) value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R(2) value of 0.56 and the lowest MSE of 174.07. CONCLUSION: The LightGBM model showed the best performance in predicting postoperative lung function.