Rule extraction from artificial neural networks remains important task in complex diseases such as diabetes and breast cancer where the rules should be accurate and comprehensible. The quality of rules is improved by the improvement of the network classification accuracy which is done by the discretization of input attributes. In this paper, we developed a rule extraction algorithm based on multiobjective genetic algorithms and association rules mining to extract highly accurate and comprehensible classification rules from ANN’s that have been trained using the discretization of the continuous attributes. The data pre-processing provides very good improvement of the ANN accuracy and consequently leads to improve the performance of the classification rules in terms of fidelity and coverage. The results show that our algorithm is very suitable for medical decision making, so an excellent average accuracy of 94.73 has been achieved for the Pima dataset and 99.36 for the breast cancer dataset.

从人工神经网络中提取规则仍然是复杂疾病（例如糖尿病和乳腺癌）中的重要任务，在这些疾病中，规则应该是准确且可理解的。规则的质量通过网络分类精度的提高而提高，网络精度通过输入属性的离散化来实现。在本文中，我们开发了一种基于多目标遗传算法和关联规则挖掘的规则提取算法，以从使用连续属性离散化训练的ANN中提取高度准确且可理解的分类规则。数据预处理可以很好地改善ANN的准确性，因此可以提高保真度和覆盖率方面的分类规则性能。