The accuracy of a diagnostic test has always been essential in detecting disease staging. Many diagnostic tests of accuracy measures are used in binary diagnosis tests. Some measures apply to multi-stage diagnosis. Yet, there are limitations to the implementation, and the performance highly depends on the distribution of diagnostic outcomes. Another essential aspect of medical diagnostic testing using biomarkers is to find an optimal cut-point that categorizes a patient as diseased or healthy. This aspect was extended to the diseases with more than two stages. We propose a diagnostic accuracy measure and optimal cut-points selection (CD), using concordance and discordance for k-stages diseases. The CD measure uses the classification agreement and disagreement between tests outcomes and diseases stages. Simulations for power studies suggest that CD can detect the differences between the null and alternative hypotheses that other methods cannot for some scenarios. Simulation results indicate that using CD measures to select optimal cut-points can provide relatively high correct classification rates than the existing measures and more balanced accurate classification rates than the generalized Youden Index (GYI). An illustration is provided using the ANDI data to choose biomarkers for diagnosing Alzheimer's Disease (AD) and select optimal cut-points for the chosen biomarkers.