Enhanced Select and Test (eST) Algorithm: Framework for Diagnosing and Monitoring Related Ailments

Abstract

Diagnosis, prediction, machine learning, and decision making are all areas of application of artificial intelligence. Particularly, intelligent (medical) diagnosis systems are now becoming pervasive providing support to healthcare delivery. However, there is a lack of precision and approximation of the algorithms driving such diagnostics systems. Though there is a number of reasoning algorithms for carrying out this diagnostic task, the precision of these diagnostic algorithms are being impaired by their reasoning structures. This paper reviews and provides an enhancement to select and test (ST) reasoning algorithm. This algorithm, adjured to be the most precise among the existing diagnostic algorithms, will be enhanced by employing the use of semantic web reasoning structures. Reasoning at the abduction, deduction, and induction levels are oriented towards rule base reasoning pattern in the semantic web. Also, a series of modularized ontology knowledge bases are stacked together in building a complex but distributed knowledge base for the entire system. The implementation of this enhanced algorithm will be used as a test-bed for diagnosing and monitoring related ailments.