Abstract
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.
Highlights
Medical Data Mining is the study of facts in medicine which change with respect to time which begins with the construction of temporal clinical databases
The lower approximation of a set refers to the elements that certainly fit into the set, and the upper approximation of a set refers to the elements that perhaps incorporate to the set in The constructive approach is used to define the lower and upper approximation operations in the form of binary relations, partitions of universe, and Boolean sub algebra
As Temporal Fuzzy Rule Based Classifier is built by the Generalization of Fuzzy Rough Sets, it's done by the lower and higher approximation operators of similarity relations; the number of human involvement is being decreased and the newly designed Classifier is simple as compared to alternative classifiers
Summary
Medical Data Mining is the study of facts in medicine which change with respect to time which begins with the construction of temporal clinical databases. The uncertain data can be well handled by the use of fuzzy rough sets, since fuzzy sets take values 0 or 1 only to indicate the degree of trueness of a hypothesis for a given time [9]. These sets are described using a pair of approximations known as upper and lower approximations. Let Pi={p1,p2,....,pn}be the set of clinical records and Ai={a1,a2,....,an}are the set of condition attributes at the time ti the lower approximation operation of a fuzzy rough set is applied to construct a temporal fuzzy rough set{SX, Pi, Ai, ti } to address the degree of uncertainty
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