The threat of lung cancer has emerged as a major global public health concern. Across Asia, lung cancer has been on the rise making it the most common cancer. Only few studies have approached this problem holistically by taking into account the social, economic, and environmental factors. Therefore, factors like air pollution, tobacco usage, socioeconomic status, employment status, marital status, and living conditions were all carefully considered in this study. To rule out the likelihood of multicollinearity, the stepwise regression and the VIF are used. Several machine learning algorithms with k-fold cross-validation were employed, including the linear regression, support vector regression, random forest, K-nearest neighbor, and cubist tree. The hyperparameter values of these algorithms are optimized using the PSO algorithm. This study showed that, in terms of the minimal RMSE and MAPE values, the random forest model with hyperparameter optimization is competitive enough for predicting the incidence rate of lung cancer in Japan, the cubist model with hyperparameter optimization for Hong Kong, the SVR model with hyperparameter optimization for South Korea and India, and the linear regression model for Singapore and Taiwan. The competitive predictive model for each country proposed in this study can aid in the accurate analysis and estimation of lung cancer incidence rates to enable the creation of efficient preventative strategies. Furthermore, through the interpretation of these predictive models, comparison of lung cancer incidence between developed and developing countries can also be done to identify differences and similarities in risk factors.

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