Abstract
BackgroundFoodborne diseases, as a type of disease with a high global incidence, place a heavy burden on public health and social economy. Foodborne pathogens, as the main factor of foodborne diseases, play an important role in the treatment and prevention of foodborne diseases; however, foodborne diseases caused by different pathogens lack specificity in clinical features, and there is a low proportion of clinically actual pathogen detection in real life.ObjectiveWe aimed to analyze foodborne disease case data, select appropriate features based on analysis results, and use machine learning methods to classify foodborne disease pathogens to predict foodborne disease pathogens that have not been tested.MethodsWe extracted features such as space, time, and exposed food from foodborne disease case data and analyzed the relationship between these features and the foodborne disease pathogens using a variety of machine learning methods to classify foodborne disease pathogens. We compared the results of 4 models to obtain the pathogen prediction model with the highest accuracy.ResultsThe gradient boost decision tree model obtained the highest accuracy, with accuracy approaching 69% in identifying 4 pathogens including Salmonella, Norovirus, Escherichia coli, and Vibrio parahaemolyticus. By evaluating the importance of features such as time of illness, geographical longitude and latitude, and diarrhea frequency, we found that they play important roles in classifying the foodborne disease pathogens.ConclusionsData analysis can reflect the distribution of some features of foodborne diseases and the relationship among the features. The classification of pathogens based on the analysis results and machine learning methods can provide beneficial support for clinical auxiliary diagnosis and treatment of foodborne diseases.
Highlights
Foodborne diseases refer to diseases caused by pathogenic factors such as harmful substances that enter the body through food intake [1]
Data analysis can reflect the distribution of some features of foodborne diseases and the relationships among the features
We presented a machine learning–based classification method for pathogens of foodborne diseases using the case data of foodborne diseases in the National Foodborne Disease Surveillance Reporting System
Summary
Background Foodborne diseases refer to diseases caused by pathogenic factors such as harmful substances that enter the body through food intake [1]. They are usually associated with contaminated foods and pathogens or viruses contained in foods. According to the Centers for Disease Control (CDC), 48 million people are infected with foodborne diseases every year in the United States, 128,000 of whom are hospitalized and 3000 of whom die [3]. Frequent occurrences of foodborne diseases at home and abroad seriously endanger public health and social economy and have become an important public health and food safety issue in the world. As the main factor of foodborne diseases, play an important role in the treatment and prevention of foodborne diseases; foodborne diseases caused by different pathogens lack specificity in their clinical features, and there is a low proportion of actual clinical pathogen detection in real life
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
