A Validation Method for the Enhanced Fuzzy Min‐Max Neural Network With Application to Heart Disease Data Classification

ABSTRACT This study investigates early detection of heart diseases using a new Enhanced Fuzzy Min‐Max (EFMM) neural network. Two key modifications are introduced: a refined EFMM contraction procedure during the learning phase to reduce data distortion and information loss, and a new weighted validation method to optimise hyperbox selection and improve prediction accuracy. The proposed model is evaluated with heart disease data sets subject to varying expansion constraints, training data sizes, and cross‐validation settings. The results indicate that the proposed EFMM model consistently outperforms other models, including the original Fuzzy Min‐Max (FMM), EFMM, and non‐FMM classifiers such as J48, MLP, IBK, AdaBoost, and others. The test accuracy rates achieve 82.11% with 5‐fold cross‐validation and 81.56% with 10‐fold cross‐validation. Compared with other FMM variants, the proposed model achieves 80.26% accuracy, surpassing GFMM, IOL‐GFMM, Kn‐EFMM, and RFMM models. Using real‐world heart disease data, the proposed model outperforms FMM and EFMM in terms of accuracy, sensitivity, and specificity. Statistical test results indicate the effectiveness of the proposed model in performance statistically, as compared with those FMM, EFMM, and other classifiers. The improved EFMM model is generic and applicable not only to heart disease diagnosis but also to other general data classification tasks.