IDENTIFICATION AND CLASSIFICATION OF FOODBORNE DISEASE OUTBREAKS

Abstract

Foodborne diseases are primarily caused by consuming contaminated food or beverages. Timely identification and classification of outbreaks are crucial to reducing illness and death. This research aims to rapidly detect causative agents to enhance food safety. Using dataset analysis, patterns were identified based on year, food type, location, and species. Classification was performed using Decision Tree, Naïve Bayes, and Random Forest algorithms. Experimental results demonstrate the effectiveness of the proposed approach in identifying and classifying outbreak patterns.