Settouti Nesma, Chikh M Amine, Saidi Meryem
Biomedical Engineering Laboratory, Tlemcen University, Tlemcen, Algeria.
Australas Phys Eng Sci Med. 2012 Sep;35(3):257-70. doi: 10.1007/s13246-012-0155-z. Epub 2012 Aug 16.
Diabetes is a type of disease in which the body fails to regulate the amount of glucose necessary for the body. It does not allow the body to produce or properly use insulin. Diabetes has widespread fallout, with a large people affected by it in world. In this paper; we demonstrate that a fuzzy c-means-neuro-fuzzy rule-based classifier of diabetes disease with an acceptable interpretability is obtained. The accuracy of the classifier is measured by the number of correctly recognized diabetes record while its complexity is measured by the number of fuzzy rules extracted. Experimental results show that the proposed fuzzy classifier can achieve a good tradeoff between the accuracy and interpretability. Also the basic structure of the fuzzy rules which were automatically extracted from the UCI Machine learning database shows strong similarities to the rules applied by human experts. Results are compared to other approaches in the literature. The proposed approach gives more compact, interpretable and accurate classifier.
糖尿病是一种身体无法调节自身所需葡萄糖量的疾病。它不允许身体产生或正常使用胰岛素。糖尿病产生的影响广泛，世界上有大量人口受其影响。在本文中，我们证明了可以获得一种具有可接受解释性的基于模糊c均值-神经模糊规则的糖尿病疾病分类器。分类器的准确性通过正确识别的糖尿病记录数量来衡量，而其复杂性通过提取的模糊规则数量来衡量。实验结果表明，所提出的模糊分类器能够在准确性和可解释性之间实现良好的平衡。此外，从UCI机器学习数据库中自动提取的模糊规则的基本结构与人类专家应用的规则有很强的相似性。将结果与文献中的其他方法进行了比较。所提出的方法给出了更紧凑、可解释且准确的分类器。
