Development of KabuTeach: a character-based decision support system for mushroom classification

Abstract

Mushroom misidentification remains a persistent public health risk in rural communities, causing poisoning and fatalities worldwide. This study presents KabuTeach, a web-based decision support system (DSS) for classifying mushrooms as edible or poisonous. The system is trained on the UCI mushroom dataset (8,124 instances, 22 categorical morphological attributes) using the categorical Naïve Bayes algorithm with Laplace smoothing (α=1.0) through the Rubix ML framework. The system employed a hybrid interactive structure and event-driven architecture with the model view–controller pattern, enabling real-time classification, human-in-the-loop expert validation, and SMS-based result delivery for low-connectivity rural environments. Model evaluation using a 70:30 holdout method achieved 99.63% accuracy, while 5-fold cross-validation yielded a mean accuracy of 99.66% (±0.1118%). Performance metrics, including a confusion matrix, per-fold results with standard deviations, precision, recall, F1-score, and specificity, confirm model robustness, attributed to the clean and discriminative categorical features of the dataset and the use of Laplace smoothing. Usability testing with 102 respondents using the USE questionnaire achieved a grand mean of 5.93 (agree). Reliance on the North American UCI dataset limits direct Philippine applicability; future work will focus on locally sourced, expert-labeled datasets. KabuTeach is a reliable, interpretable, and user-centered DSS that bridges traditional knowledge and modern ICT for safer rural mushroom foraging.