An Explainable Heterogeneous Stacking Ensemble Framework for Fish Health Prediction in Intelligent Aquaculture

Abstract

Aquaculture is a vital element of global food security and economic sustainability. The changing environment from time to time and the rapid spread of diseases in aquaculture leading to huge economic losses, still pose a major challenge in the maintenance of healthy fish. Timely intervention for rapid and accurate fish health forecasting and diagnosis will decrease the fish mortality and increase the efficiency of fish production. In the current paper, a stacking model using heterogenous ensemble learning will be developed using three machine learning algorithms including the Random Forest algorithm, Support Vector Machine (SVM) and XGBoost together with Logistic Regression as the meta-classifier to predict fish health. Data preprocessing stage should be conducted prior to developing a model which will include missing value treatment, feature scaling, feature encoding and irrelevant feature elimination. The efficiency and generality of the proposed model will be estimated via using stratified 10-fold cross-validation technique. The quality of the model will be estimated by using the criteria including accuracy, precision, recall, F1 Score, confusion matrix and ROC curve analysis. In addition, SHapley Additive exPlanations will be applied to increase the explainability of the proposed model by showing the contribution of each environmental and biological factor into predicting fish health. The experimental results reveal that the heterogeneous stacking model can outperform the individual base learners and provide a transparent decision explanation. The framework is proposed to be utilized for intelligent fish health monitoring and contributing to sustainable aquaculture through predicting diseases in time and managing the aquaculture farms effectively, interpretable and scalable way.