A Novel Rule Based Classifier for Mining Temporal Medical Databases Using Fuzzy Rough Set Approach

This paper proposes a new Rule Based Classifier that uses fuzzy rough set and temporal logic in order to mine temporal patterns in clinical databases. The lower approximation hypothesis and fuzzy decision table with the fuzzy features are utilized to obtain fuzzy decision classes for constructing the classifier. By considering a subset of attributes, including the temporal intervals the lower approximations are designed in this work, in which temporal intervals are also considered. 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 neuro fuzzy rules with Allen's temporal algebra to induce rules (patterns). Eventually 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 sternness of the disease and precision in decision support system.