Research Article Open Access

A Dynamic Temporal Neuro Fuzzy Inference System for Mining Medical Databases

R. Sethukkarasi1 and A. Kannan1
  • 1 Anna University, India
Journal of Computer Science
Volume 8 No. 11, 2012, 1924-1931

DOI: https://doi.org/10.3844/jcssp.2012.1924.1931

Submitted On: 29 February 2012 Published On: 7 November 2012

How to Cite: Sethukkarasi, R. & Kannan, A. (2012). A Dynamic Temporal Neuro Fuzzy Inference System for Mining Medical Databases. Journal of Computer Science, 8(11), 1924-1931. https://doi.org/10.3844/jcssp.2012.1924.1931

Abstract

The analysis and representation of temporal data are becoming increasingly important in many areas of research and application. The existing Fuzzy Cognitive Maps (FCMs) are efficient modeling method for knowledge representation and fuzzy reasoning in time series analysis. In the past, it was used to represent a complex causal system as a collection of concepts and causal relationships among concepts. However, most of the FCMs available now are constructed manually and are constrained with human experts' intervention for assessing its reliability. This study proposes a new temporal mining system to discover temporal dependencies between the concepts of a complex causal system by building a Fuzzy Temporal Cognitive Map (FTCM) by extending the FCM. For this purpose, a four-layer fuzzy temporal neural network is proposed and implemented by the automatic creation of the conventional FTCMs from the given data. This FTCM is generated from the medical temporal database records of diabetic patients where the medical diagnosis is performed by converting the fuzzy cognetive maps into a fuzzy temporal rule based inference system using Allen's temporal relationships and fuzzy temporal rules.

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Keywords

  • Fuzzy Temporal Cognitive Maps (FTCMs)
  • Data Mining
  • Time Series Analysis
  • Fuzzy Neural Network
  • Temporal Relationships
  • Fuzzy Temporal Rules
  • Inferencing System