Establishing a Combination Model in Predicting Mortality of Lung Cancer

Lung Cancer
17/07/2020

Sichuan Da Xue Xue Bao Yi Xue Ban. 2018 Nov;49(6):960-962.

ABSTRACT

OBJECTIVE: To identify a good combination model for predicting the mortality of lung cancer.

METHODS: Mortality data of lung cancer from 2001-2013 were used to test three prediction model: dynamic series, exponential smoothing, and Joinpoint regression. Weight coefficients of the combination models were calculated using the arithmetic average method, the variance inverse method, the mean square error inverse method, and the simple weighted average method.

RESULTS: The exponential smoothing model had the highest accuracy (79.67%) of prediction, followed by the Joinpoint linear model (74.27%). The combination of these two models resulted in better results. The arithmetic average method and the mean square error inverse method had the best prediction, with an accuracy of 86.87% and 85.80%, respectively.

CONCLUSIONS: The combined model has higher accuracy than the single models in predicting the mortality of lung cancer.