A Design of the Group Decision Making Medical Diagnosis Expert System Based on SED-JD Algorithm

Medical expert system not only has a lot of medical professional knowledge, but also has inference ability. The inference engine is not only one of the cores of the expert system, but also the key to designing the expert system. We focus on inference engine. In order to improve the diagnostic accuracy of medical diagnostic expert system, we propose the Group Decision Making (GDM) medical diagnosis expert system based on the Standardized Euclidean Distance-Jaccard Distance (SED-JD) algorithm. The mainly research content of inference engine is similarity measurement algorithm (that is SED-JD) and inference engine rule scheme (that is GDM). In order to get more accurate diagnosis, data preprocessing was performed before our experiments. In the design of inference engine, the selection of the Group Decision Making Objects (GDMOs) depends on the maximum similarity distance (MaxDist). The final decision result depends on the average similarity distance of each subgroup. By comparing the similarity scheme and GDM scheme, the experimental results show that GDM scheme is more effective and accurate. By comparing the Standardized Euclidean Distance (SED) algorithm, the Jaccard Distance (JD) algorithm and SED-JD algorithm, the experimental results show that SED-JD algorithm is more accurate.