International Journal of Public Health and Safety

ISSN: 2736-6189

Open Access

Improved Random Forest-Based Risk Prediction Model for Food Safety with Virtual Sample Integration


Qinzhi Wang*

Food safety is an important concern for consumers and food producers alike. With the increasing complexity of the food supply chain and the global nature of the food industry, the risk of foodborne illness has become a major public health issue. Risk prediction models are an important tool for food safety management, as they can help identify high-risk products, processes, and supply chains. In this article, we will discuss an improved random forest-based risk prediction model for food safety with virtual sample integration. Random forest is a popular machine learning algorithm that is widely used for risk prediction in various fields, including healthcare, finance, and ecology. Random forest is an ensemble learning method that combines multiple decision trees to generate a robust and accurate prediction model. In food safety, random forest has been used to predict the risk of foodborne illness based on various factors, such as food type, production process, and contamination history.


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