PREDICTION PERFORMANCE OF DECISION TREE INDUCERS ON AUGMENTED BACILLUS CEREUS GROWTH DATA

Accurate prediction of microbial growth is of great importance in critical areas such as food safety and environmental sciences. In this study, a hybrid of mathematical methods and machine learning-based approaches are used to model the growth dynamics of foodborne pathogen Bacillus cereus. Since the use of mathematical models alone does not sufficiently cover the non-linear data structure of bacterial systems, better results are obtained when hybrids are used together with machine learning methods. We examine the results of five different tree-based models for predicting the growth of Bacillus cereus, namely Fine Tree, Medium Tree, Coarse Tree, Ensemble Boosted Trees and Ensemble Bagged Trees. We evaluate each model with performance metrics such as Root Mean Square Error (RMSE), Mean Square Error (MSE), R² and Mean Absolute Error (MAE). The results show that the Ensemble Bagged Trees model performs the best, with a validation RMSE of 0.0094 and an R² value of 0.9995. Also, the Fine Tree model has an R² value of 0.9990. In general, ensemble methods offer significant advantages in prediction accuracy.