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Chronic diseases are the main cause for death in the world. Among all risk factors concerning chronic diseases, those related to an unhealthy diet are most important. Although much research was done on dietary behavior, there are only few quantitative studies on the relationship between dietary taste and chronic diseases. In this article, a taste dataset of the major categories of Chinese cuisine is established based on crowdsourced data from Chinese recipe websites. For a quantitative analysis of people's taste in different regions, additionally the locations of restaurants by category (using their respective points of interest) are integrated. Using the software Geodetector, these regional taste preferences are then correlated with the three chronic diseases, hemorrhagic stroke, pancreatic cancer, and upper respiratory tract infection. For all the three diseases, the results indicate very salty, moderate sweet and very spicy food as the primary risk factors. Also, the degree of sweetness is not linear with the risk of pancreatic cancer. These results are statistically significant. In this study, a quantitative method on discovering potential health risk factors based on mining of crowdsourced data is proposed for the first time. This method can be applied before disease- related experiments to filter potential factors, and it is helpful for the public health department to make quick corresponding intervention policies.

Chronic diseases are the main cause for death in the world. Among all risk factors concerning chronic diseases, those related to an unhealthy diet are most important. Although much research was done on dietary behavior, there are only few quantitative studies on the relationship between dietary taste and chronic diseases. In this article, a taste dataset of the major categories of Chinese cuisine is established based on crowdsourced data from Chinese recipe websites. For a quantitative analysis of people's taste in different regions, additionally the locations of restaurants by category (using their respective points of interest) are integrated. Using the software Geodetector, these regional taste preferences are then correlated with the three chronic diseases, hemorrhagic stroke, pancreatic cancer, and upper respiratory tract infection. For all the three diseases, the results indicate very salty, moderate sweet and very spicy food as the primary risk factors. Also, the degree of sweetness is not linear with the risk of pancreatic cancer. These results are statistically significant. In this study, a quantitative method on discovering potential health risk factors based on mining of crowdsourced data is proposed for the first time. This method can be applied before disease- related experiments to filter potential factors, and it is helpful for the public health department to make quick corresponding intervention policies.