Determining the most efficient use of diagnostic tests is one of the complex issues facing medical practitioners. With the soaring cost of healthcare, particularly in the US, there is a critical need for cutting costs of diagnostic tests, while achieving a higher level of diagnostic accuracy. This paper develops a learning based methodology that, based on patient information, recommends test(s) that optimize a suitable measure of diagnostic performance. A comprehensive performancemeasure is developed that accounts for the costs of testing, morbidity, and mortality associated with the tests, and time taken to reach diagnosis. The performancemeasure also accounts for the diagnostic ability of the tests. The methodology combines tools from the fields of data mining (rough set theory, in particular), utility theory, Markov decision processes (MDP), and reinforcement learning (RL). The rough set theory is used in extracting diagnostic information in the form of rules from the medical databases. Utility
