Cheruku Ramalingaswamy, Edla Damodar Reddy, Kuppili Venkatanareshbabu
Department of Computer Science and Engineering, National Institute of Technology Goa, Ponda, Goa 403401, India.
Comput Biol Med. 2017 Feb 1;81:79-92. doi: 10.1016/j.compbiomed.2016.12.009. Epub 2016 Dec 19.
Diabetes is a major health challenge around the world. Existing rule-based classification systems have been widely used for diabetes diagnosis, even though they must overcome the challenge of producing a comprehensive optimal ruleset while balancing accuracy, sensitivity and specificity values. To resolve this drawback, in this paper, a Spider Monkey Optimization-based rule miner (SM-RuleMiner) has been proposed for diabetes classification. A novel fitness function has also been incorporated into SM-RuleMiner to generate a comprehensive optimal ruleset while balancing accuracy, sensitivity and specificity. The proposed rule-miner is compared against three rule-based algorithms, namely ID3, C4.5 and CART, along with several meta-heuristic-based rule mining algorithms, on the Pima Indians Diabetes dataset using 10-fold cross validation. It has been observed that the proposed rule miner outperforms several well-known algorithms in terms of average classification accuracy and average sensitivity. Moreover, the proposed rule miner outperformed the other algorithms in terms of mean rule length and mean ruleset size.
糖尿病是全球面临的一项重大健康挑战。现有的基于规则的分类系统已被广泛用于糖尿病诊断，尽管它们必须克服在平衡准确性、敏感性和特异性值的同时生成全面最优规则集这一挑战。为了解决这一缺点，本文提出了一种基于蜘蛛猴优化的规则挖掘器（SM-RuleMiner）用于糖尿病分类。一种新颖的适应度函数也被纳入到SM-RuleMiner中，以在平衡准确性、敏感性和特异性的同时生成全面最优规则集。在皮马印第安人糖尿病数据集上使用10折交叉验证，将所提出的规则挖掘器与三种基于规则的算法（即ID3、C4.5和CART）以及几种基于元启发式的规则挖掘算法进行比较。结果发现，所提出的规则挖掘器在平均分类准确性和平均敏感性方面优于几种著名算法。此外，所提出的规则挖掘器在平均规则长度和平均规则集大小方面也优于其他算法。
