A decision support system for mushroom classification using Naïve Bayesian algorithm

Abstract

Mushrooms are rich in vitamins and proteins, a well-known superfood, however, cases of harmful mushroom consumption worldwide result in hallucinations, illness, or death. A significant challenge is that some poisonous mushrooms closely resemble edible varieties, making it difficult for mushroom foragers to distinguish between them. This study introduced KabuTeach, a decision support system (DSS) designed to classify mushrooms based on their morphological characteristics using the Naïve Bayes (NB) algorithm. The classification model was applied to a real-world dataset of 8,124 instances from Kaggle, containing 23 attributes. Evaluation metrics, including accuracy, recall, precision, specificity, and F1-score, were used to assess the classifier’s performance. Results indicated that the NB classification algorithm integrated into KabuTeach achieved a high accuracy level of 89.13%, using a 70:30 data split and 5-fold cross-validation approaches. The 0.98 AUC (area under the curve) value further concluded that the model was excellent in classifying between edible and poisonous mushrooms. These findings showed that KabuTeach is a reliable classification tool that aids mushroom foragers in differentiating mushrooms and promoting safer consumption practices. This innovation in agricultural technology could potentially reduce health risks by minimizing accidental ingestion of toxic mushrooms, ultimately contributing to public health safety.