FUZZY CLASSIFICATION OF PATIENT STATE WITH APPLICATION TO ELECTRODIAGNOSIS OF PERIPHERAL POLYNEUROPATHY

A methodology which accounts for uncertainty or imprecision in experim ental observations and both norm and pathology definitions is develope d on the basis of a distance measure between fuzzy numbers. These fuzz y numbers may represent, respectively, the measurements, norm, and pat hology. The distance measure, called normalized fuzzy pathology index (NFPI), evaluates the difference of distance between observed experime ntal values for a given patient and norm on the one hand, and patholog y on the other hand. The NFPI characterizes patient state as a continu ous index; however, to conform to medical usage, categories of values are defined. Each of these categories corresponds to a linguistic vari able. The case study used to illustrate the methodology is the electro diagnosis of peripheral polyneuropathy in diabetic patients. Here, fou r initial linguistic categories are defined by a physician, namely: no rmal state, borderline state, clear-cut, and severe pathology. The NFP I is calculated in three cases that provide a sensitivity analysis on measurement fuzziness and distance function weighting. The model is ca librated using 203 cases and validated using 291 different cases. The results correspond very closely to the physician's diagnosis. The loss of information in discretizing the continuous state of patients is di scussed. Transferring this fuzzy approach to other cases where the con cept of distance is relevant offers no difficulty.