Soft computing considerations on diagnostic accuracy of multiple tests

In order to find the optimum combination of multiple tests, this paper considers how diagnostic accuracy changes with the number of tests and with five kinds of combination methods: (A) sequential tests, (B) sequential tests after changing the order in (A), (C) simultaneous tests using the believe-the-negative rule, (D) simultaneous tests using the believe-the-positive rule, and (E) simultaneous tests using the believe-all-positives-and-all-negatives rule. The diagnostic accuracy is defined by sensitivity (α) and specificity (β). An overall 2×2 table for multiple tests was inducted from the 2×2 tables for each test. The overall diagnostic accuracy was calculated from the overall 2×2 table and was expressed by a generalized formula. As it was proved that the combination methods A, B and C are "equivalent", the five methods could be summarized into three methods: (1) for A, B and C, β increased but α decreased with an increasing number of subjects n; (2) for D, α increased but β decreased with n; (3) for E, α and β increased with n, although a problem exists concerning how to judge the remaining subjects who are between positive-in-all-tests and negative-in-all-tests. Soft computing considerations ware adopted for this problem