A Dynamic Temporal Neuro Fuzzy Inference System for Mining Medical Databases
- 1 Anna University, India
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.
DOI: https://doi.org/10.3844/jcssp.2012.1924.1931
Copyright: © 2012 R. Sethukkarasi and A. Kannan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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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