The objective of this study was to develop markedly improved risk prediction models for lung cancer using a prospective cohort of 395,875 participants in Taiwan. Discriminatory accuracy was measured by generation of receiver operator curves and estimation of area under the curve (AUC). In multivariate Cox regression analysis, age, gender, smoking pack-years, family history of lung cancer, personal cancer history, BMI, lung function test, and serum biomarkers such as carcinoembryonic antigen (CEA), bilirubin, alpha fetoprotein (AFP), and c-reactive protein (CRP) were identified and included in an integrative risk prediction model. The AUC in overall population was 0.851 (95% CI = 0.840-0.862), with never smokers 0.806 (95% CI = 0.790-0.819), light smokers 0.847 (95% CI = 0.824-0.871), and heavy smokers 0.732 (95% CI = 0.708-0.752). By integrating risk factors such as family history of lung cancer, CEA and AFP for light smokers, and lung function test (Maximum Mid-Expiratory Flow, MMEF_25-75%), AFP and CEA for never smokers, light and never smokers with cancer risks as high as those within heavy smokers could be identified. The risk model for heavy smokers can allow us to stratify heavy smokers into subgroups with distinct risks, which, if applied to low-dose computed tomography (LDCT) screening, may greatly reduce false positives.