Induction of diagnostic test strategies with multi-level information measures

This paper presents a method for inducing clinical diagnostic test protocols or strategies from data. We represent testing strategies as a strategy tree. To support induction of strategy tree, we define three information measures: K-level information, K-level information gain, K-level gain ratio, and K-level cost index, for test selection during strategy building. These measures generalize Quinlan's information measures used in decision tree induction. We present theoretical and experimental results to show that the K-level cost index can be used to induce strategy trees in a practical domain.