Background <p>Preference-based measures of health-related quality of life (HRQoL), such as the Short Form Six-Dimension (SF-6D) is essential for health economic evaluations. However, these measures are rarely included in clinical trials for lung cancer. This study aims to develop mapping algorithms to predict SF-6D health utility scores from the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core (EORTC QLQ-C30) and Quality of Life Questionnaire-Lung Cancer 13 (QLQ-LC13).</p> Method <p>The study sample comprised a Chinese population with lung cancer (<i>n</i> = 625). Traditional regression techniques, including Ordinary Least Squares regression, Generalized Linear Model, as well as machine learning techniques, such as Gradient Boosting Tree, Support Vector Regression, Ridge Regression are used. Five-fold cross-validation was performed. The performance metrics used to evaluate the models including <i>R</i>^<i>2</i>, root mean square error (<i>RMSE</i>),mean absolute error (<i>MAE</i>) and mean absolute percentage error (<i>MAPE)</i> were used to screen the optimal model.</p> Results <p>The mean and median of SF-6D health utility values were 0.774 (SD = 0.154) and 7.795, respectively. The model with the best mapping performance was the Ridge regression model Five-fold cross-validation (<i>CV</i>) results show that the Ridge regression model has the best mapping performance, the final prediction indexes are <i>R</i>^<i>2</i> <i>= 0.753</i>, <i>RMSE = 0.074</i>, <i>MAE = 0.057</i>,<i> MAPE = 8.169%</i>.</p> Conclusions <p>This study developed an optimized mapping algorithm to predict the utility index from the QLQ-C30 QLQ-LC13 to the SF-6D. This algorithm offers provides an effective alternative for estimating SF-6D estimation when the preference-based health utility values are unavailable.</p>