Generating Quality IF-THEN Rules for Diabetes using Linguistic Summarization

Abstract

Linguistic summarization is an approach of extraction of knowledge or linguistic patterns from datasets. Since summaries are brief, they promote quick analysis of data. Numerous researchers have utilized this approach to produce summaries which are easy to comprehend. Furthermore, linguistic summarization has been used to generate IF-THEN rules in the literature which not only convey data easily but is utilized in making decisions. In this paper we follow this approach to generate IF-THEN rules for diabetes on a dataset. This constructed dataset consists of responses from individuals for five parameters, crucial in the diagnosis of diabetes. Consequently, the quality of the rules produced using linguistic summarization is checked by the four quality measures namely: degree of truth, coverage, reliability and outliers. Among these, the degree of reliability is useful to find rules that represent dataset completely, and the outliers are used to find rules that deviate from the original result. Our experiment reveals results that are promising when compared to the PIMA dataset.