A hybrid intelligent system for medical data classification

Manjeevan Seera, Chee Peng Lim

Research output: Contribution to journalArticleResearchpeer-review

198 Citations (Scopus)

Abstract

In this paper, a hybrid intelligent system that consists of the Fuzzy Min-Max neural network, the Classification and Regression Tree, and the Random Forest model is proposed, and its efficacy as a decision support tool for medical data classification is examined. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. It is able to learn incrementally from data samples (owing to Fuzzy Min-Max neural network), explain its predicted outputs (owing to the Classification and Regression Tree), and achieve high classification performances (owing to Random Forest). To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Breast Cancer Wisconsin, Pima Indians Diabetes, and Liver Disorders from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity, as well as the area under the Receiver Operating Characteristic curve are computed. The results are analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system is effective in undertaking medical data classification tasks. More importantly, the hybrid intelligent system not only is able to produce good results but also to elucidate its knowledge base with a decision tree. As a result, domain users (i.e., medical practitioners) are able to comprehend the prediction given by the hybrid intelligent system; hence accepting its role as a useful medical decision support tool.

Original languageEnglish
Pages (from-to)2239-2249
Number of pages11
JournalExpert Systems with Applications
Volume41
Issue number5
DOIs
Publication statusPublished - Apr 2014
Externally publishedYes

Keywords

  • Classification and regression tree
  • Fuzzy Min-Max neural network
  • Hybrid intelligent systems
  • Medical decision support
  • Random forest

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