Adaptive fuzzy logic-based framework for handling imprecision and uncertainty in classification of bioinformatics datasets

Tarek Helmy*, Zehasheem Rasheed, Mohamed Al-Mulhem

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Classification in the emerging field of bioinformatics is a challenging task, because the information about different diseases is either insufficient or lacking in authenticity as data is collected from different types of medical equipments. In addition, the limitation of human expertise in manual diagnoses leads to incorrect diagnoses. Moreover, the information gathered from various sources is subject to imprecision and uncertainty. Imprecision arises when the data is not validated by experts. This paper presents an adaptive Type-2 Fuzzy Logic System-based (FLS) classification framework for multivariate data to diagnose different types of diseases. This framework is capable of handling imprecision and uncertainty, and its classification accuracy and performance are measured by using University of California Irvine (UCI), well-known medical data sets. The results are compared with the most common existing classifiers in both computer science and statistics literatures. This classification is performed based on the nature of inputs (e.g., singleton or nonsingleton) and on whether uncertainty is present in the system or absent. Empirical results have shown that our proposed FLS classification framework outperforms earlier implemented models with better classification accuracy. In addition, we conducted empirical studies on this classifier regarding the impact of various parameters of FLS such as training algorithms and defuzzification methods.

Original languageEnglish
Pages (from-to)513-534
Number of pages22
JournalInternational Journal of Computational Methods
Issue number3
StatePublished - Sep 2011

Bibliographical note

Funding Information:
We would like to thank King Fahd University of Petroleum and Minerals for supporting this work through the funded research project # IN090023 and providing the computing facilities. Special thanks to the anonymous reviewers for their valuable comments that enhanced this paper. Thanks extend to Mr. David Birkett for his help in proofreading the paper.


  • Adaptive fuzzy logic
  • bioinformatics
  • classification

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computational Mathematics


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