Data-driven selection of multi-criteria decision-making methods and its application to diagnosis of thyroid nodules

Abstract

For modeling and analyzing multi-criteria decision-making (MCDM) problems, selecting an MCDM method is an important issue. To address this issue, three methods that use interval numbers, fuzzy linguistic numbers, and belief distributions are compared based on historical decision data. Three ways are designed to flexibly compare the three methods. In the first way, criterion weights are learned from the similarity between the individual assessments and the overall assessments. In the second way, criterion weights are learned from minimizing the difference between the overall assessments and the aggregated assessments derived from the individual assessments. In the third way, three sets of criterion weights and a set of method weights are learned from minimizing the difference between the overall assessments and the aggregated results. The aggregated results are derived from unifying and combining the aggregated assessments generated using the three methods. The transformation among interval numbers, fuzzy linguistic numbers, and belief distributions is designed for their unification and a fair comparison among the three methods. The three methods are applied to help diagnose thyroid nodules for five radiologists from a tertiary hospital located in Hefei, Anhui, China. Based on the historical examination reports provided by the five radiologists, the three developed ways are used to compare the three methods. Experimental results reveal two findings. One finding is that different ways will produce different choices among the three methods. While the other is that the highest decision accuracy for the five radiologists is associated with the combination of the MCDM method and the comparison way.