In this paper, we propose a new pattern classification system by combining Temporal features with Fuzzy Min-Max (TFMM) neural network based classifier for effective decision support in medical diagnosis. Moreover, a Particle Swarm Optimization (PSO) algorithm based rule extractor is also proposed in this work for improving the detection accuracy. Intelligent fuzzy rules are extracted from the temporal features with Fuzzy Min-Max neural network based classifier, and then PSO rule extractor is used to minimize the number of features in the extracted rules. We empirically evaluated the effectiveness of the proposed TFMM-PSO system using the UCI Machine Learning Repository Data Set. The results are analysed and compared with other published results. In addition, the detection accuracy is validated by using the tenfold cross validation.

Representation of temporal knowledge and analysis of temporal data is becoming a good practice for effective classification and prediction. Various semantic levels on knowledge representation schemes have been measured for temporal data. The existing Fuzzy Cognitive Maps (FCMs) facilitate modeling dynamic systems for knowledge representation and reasoning under uncertainty. However, the FCMs are constructed manually and are constrained by the human experts' validation for assessing its reliability and they are lacking in considering temporal features necessary for reasoning in medical applications. This paper proposes a new temporal mining system known as Fuzzy Temporal Cognitive Map (FTCM), which defines a complete discrete temporal extension and fuzzy inference mechanism of FCM. In FTCM, the temporal dependencies of concepts during a particular time interval are measured. This work aims to reduce the complexities of dynamic modeling of a complex causal system by proposing a four layer fuzzy neural network to construct FTCM from the temporal data. In this proposed model, a fuzzy temporal mutual subsethood operator is used to measure the activation spread in the FTCM for automatic quantification of causalities. This FTCM is designed for a set of temporal clinical records, which can be further used for inferencing and prediction in medical diagnosis by generating a set of fuzzy temporal rules using Allen's temporal relationships and fuzzy temporal rules.

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