SM-RuleMiner: Spider monkey based rule miner using novel fitness function for diabetes classification

• Published in: Computers in biology and medicine Vol. 81; pp. 79 - 92
• Main Authors: Cheruku, Ramalingaswamy, Edla, Damodar Reddy, Kuppili, Venkatanareshbabu
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.02.2017; Elsevier Limited
• Subjects: Accuracy; Algorithms; Animals; Ateles; Atelinae; Biomimetics - methods; Classification; Classification rules; Data mining; Data Mining - methods; Decision Support Systems, Clinical - organization & administration; Diabetes; Diabetes diagnosis; Diabetes Mellitus - classification; Diabetes Mellitus - diagnosis; Diagnosis, Computer-Assisted - methods; Discriminant analysis; Electronic Health Records - organization & administration; Heuristic; Humans; Insulin resistance; Internal Medicine; Optimal ruleset; Optimization algorithms; Optimization techniques; Other; Reproducibility of Results; Researchers; Rule-based algorithms; Sensitivity and Specificity; Spider monkey optimization; Spider monkey optimization; Optimal ruleset; Classification rules; Diabetes diagnosis; Rule-based algorithms
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2016.12.009

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. •SM-RuleMiner has been designed for rule mining task on diabetes data.•Proposed novel fitness function allowed to generate minimal and precise rules.•Proposed SM-RuleMiner achieved best rank in predictive accuracy and sensitivity.