Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Advertisement

Currently submitted to: JMIR Medical Informatics

Date Submitted: Oct 11, 2020
Open Peer Review Period: Oct 11, 2020 - Dec 6, 2020
(currently open for review)

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Prediction of Foodborne Diseases Pathogens: A Machine Learning Approach

  • Hanxue Wang; 
  • Wenjuan Cui; 
  • Yunchang Guo; 
  • Yi Du; 
  • Yuanchun Zhou

ABSTRACT

Background:

Foodborne 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 the clinical features, then there is a low proportion of clinically actual pathogen detection in real life.

Objective:

Analyzing the data of foodborne disease cases, selecting appropriate features based on the analysis results, and using machine learning methods to classify foodborne disease pathogens, so as to predict the pathogens of foodborne diseases which have not been tested.

Methods:

Extracting features such as space, time, and food exposure from the data of foodborne disease cases, analyzing the relationship between these features and the pathogens of foodborne diseases, using a variety of machine learning methods to classify the pathogens of foodborne diseases, and comparing the results to obtain the optimal pathogen prediction model with the highest accuracy.

Results:

By comparing the results of four models we used, the GBDT model obtains the highest accuracy, which is almost 69% in identifying four pathogenic bacteria including Salmonella, Norovirus, Escherichia coli, and Vibrio parahaemolyticus. And by evaluating the importance of features, we find that the time of illness, geographical longitude and latitude, diarrhea frequency and so on, play important roles in classifying the foodborne disease pathogens.

Conclusions:

Related data 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.


 Citation

Please cite as:

Wang H, Cui W, Guo Y, Du Y, Zhou Y

Prediction of Foodborne Diseases Pathogens: A Machine Learning Approach

JMIR Preprints. 11/10/2020:24924

DOI: 10.2196/preprints.24924

URL: https://preprints.jmir.org/preprint/24924

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.