Fuzzy rule-driven data mining framework for knowledge acquisition for expert system

Abstract

Abstract An expert system is one of the most popular artificial intelligence applications, and replication of human knowledge and expertise to the knowledge-based expert system, which is called knowledge acquisition, is the most difficult task and problematic bottleneck in the course of development and implementation of the system. Although various techniques had been developed for knowledge acquisition such as interview, questionnaire, machine learning techniques, among others, the problem is not fully addressed. This study proposes a new framework termed fuzzy rule-driven data mining framework to knowledge acquisition that addresses the problematic bottleneck of knowledge transfer/acquisition from a human domain expert to the knowledge-based expert system. The framework focuses on extracting useful and novel knowledge from a historical dataset using an improved C4.5 decision tree classification algorithm as a set of crisp rules for subsequent transformation into a set of fuzzy rules with the help of human experts to capture their opinion about the data of the problem domain. The framework was applied in a real-life application, where an expert system for diagnosis of coronary artery diseases (CADs) using a Nigerian-based dataset was developed to validate and evaluate efficiency of the framework. The data mining algorithm used for extracting useful and novel knowledge from the historical dataset has the best accuracy of 86.56% compared to C4.5, which appears to have 83.99%, and random tree with 82.81% accuracy, respectively. In contrast, the expert system developed with the framework called fuzzy rule-driven data mining expert system (FRDDM-ES) for diagnosis of CADs has 93.50% specificity and 90.20% sensitivity with overall accuracy of 91.95%.