A machine learning-driven early warning system for cryptocaryoniasis in marine aquaculture.

ABSTRACT:

Background

Disease outbreaks, particularly cryptocaryoniasis caused by the ciliate Cryptocaryon irritans, pose significant barriers to sustainable marine fish aquaculture, undermining productivity, profitability, and biosecurity. Despite its impact, early warning tools for parasitic diseases leveraging advanced technologies remain underdeveloped.

Methods

We developed a machine learning (ML)-driven early warning system for cryptocaryoniasis, integrating seven years of outbreak surveillance data (n = 429 events from 2016 to 2023) with 17 high-resolution oceanographic predictors influencing parasite life cycles along China's coast. Five supervised ML models: logistic regression (LR), support vector machine (SVM), random forest (RF), XGBoost (XGB), and artificial neural network

SUBMITTER: Xie X 

PROVIDER: S-EPMC12659040 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature