Abstract
Lung cancer is still one of the most common causes of death around the world, while there is overwhelming evidence that the environment and lifestyle factors are predominant causes of most sporadic cancers. However, when applying human-behaviour indicators to the prediction of cancer mortality (CM), we are often caught in a dilemma with inadequate sample size. Thus, this study extracted 30 human-behaviour indicators of seven categories (air pollution, tobacco smoking & alcohol consumption, socioeconomic status, food structure, working culture, medical level, and demographic structure) from Organization for Economic Cooperation and Development Database and World Health Organization Mortality Database for 13 countries (1998–2013), and employed Support Vector Machine (SVM) to examine the weights of 30 indicators across the 13 countries and the power for predicting lung CM for the years between 2014–2016. The weights of different human-behaviour indicators indicate that every country has its own lung cancer killers, that is, the human-behaviour indicators are country specific; Moreover, SVM has an excellent power in predicting their lung CM. The average accuracy in prediction offered by SVM can be as high as 96.08% for the 13 countries tested between 2014 and 2016.
Highlights
Socioeconomic level[2], medical level[9] were important factors contributing to cancer mortality (CM)
The design of Experiment 1 was derived from Sensitivity Analysis (SA), which is used to explore a mathematic model Y = f(X1, ..., Xk), for how the output Y varies as the input Xi varies
The results of Experiment 1 suggested that the fluctuations of lung CM in different countries were caused by different human-behaviour indicators, indicating that a causal direction from shifts in daily human behaviours to those in lung CM did exist
Summary
Socioeconomic level[2], medical level[9] were important factors contributing to CM. There may be some cultural factors which might not be directly relevant, but substantially effective in a predicting model; for instance, working culture in Eastern Europe was much heavier than that in the western part, which caused the participants in Eastern Europe lacking time for exercising[10,11]. If we lose consideration for any one of these factors, the results obtained could not become persuasive enough. It is crucial to picture the human-behaviour trends globally as a whole by taking both the temporal patterns and future trends into consideration for predicting lung CM in future
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
