biostudies-literatureXie XZhejiang Province "Three Rural Nine-Party" Science and Technology Cooperation PlanThe Norwegian Agency for Shared Services in Education and ResearchInternational Cooperation Project of Ningbo CityNingbo Welfare Project490https://www.ebi.ac.uk/biostudies/studies/S-EPMC12659040biostudies-literatureUnknown18(1)<h4>Background</h4>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.<h4>Methods</h4>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 Parasites & vectorsA machine learning-driven early warning system for cryptocaryoniasis in marine aquaculture.PMC126590400002023H0152024S1422023SNJF071Jiang YLi YYin FXie XZhou SBuchmann KWang XGalindo-Villegas JZhang BfalseA machine learning-driven early warning system for cryptocaryoniasis in marine aquaculture.<h4>Background</h4>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.<h4>Methods</h4>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 2025-01-01T00:00:00Z2025 Nov2026-06-05T21:40:15.608Z2026-05-22T03:15:56.238ZS-EPMC126590404129961810.1186/s13071-025-07124-z