A Rough Set Based Fuzzy Inference System for Mining Temporal Medical Databases

The main objective of this research work is to construct a Fuzzy Temporal Rule Based Classifier that uses fuzzy rough set and temporal logic in order to mine temporal patterns in medical databases. The lower approximation concepts and fuzzy decision table with the fuzzy features are used to obtain fuzzy decision classes for building the classifier. The goals are pre-processing for feature selection, construction of classifier, and rule induction based on increment rough set approach. The features are selected using Hybrid Genetic Algorithm. Moreover the elementary sets are obtained from lower approximations are categorized into the decision classes. Based on the decision classes a discernibility vector is constructed to define the temporal consistency degree among the objects. Now the Rule Based Classifier is transformed into a temporal rule based fuzzy inference system by incorporating the Allen’s temporal algebra to induce rules. It is proposed to use incremental rough set to update rule induction in dynamic databases. Ultimately these rules are categorized as rules with range values to perform prediction effectively. The efficiency of the approach is compared with other classifiers in order to assess the accuracy of the fuzzy temporal rule based classifier. Experiments have been carried out on the diabetic dataset and the simulation results obtained prove that the proposed temporal rule-based classifier on clinical diabetic dataset stays as an evidence for predicting the severity of the disease and precision in decision support system.