A novel generalized belief structure comprising unprecisiated uncertainty applied to aphasia diagnosis

Our basic understanding of evidential reasoning, in its vast and intricate complexity, has been guided by different methods including probabilistic and possibilistic models. However, restrictions surrounding evidence must be fully precisiated/validated before discussing their probability or possibility which is rarely achieved, especially in uncertain or imprecise environments. In this paper, a new generalization of the Dempster-Shafer Theory (DST) is presented that accounts for this issue. In the proposed generalization, we include information about the validity of a fuzzy body of evidence to represent its bearing on unprecisiated information, and then we distribute this knowledge over the belief structure. We provide an epistemic framework of evidence and study its behavior and its constraints in terms of validity, probability, and possibility to establish a foundation for evidential reasoning and unprecisiated uncertainty that exists in evidence. The suggested belief structure is crafted by min-max optimization method. Then, the proposed structure is used to estimate the probability density function (pdf) and is explored by the application diagnosis of aphasia, in which unprecisiated uncertainty is involved due to the subjectivity of the test items, inconsistency in the interpretation of aphasic syndromes, and the changeable precision of medical tests. Even in requiring more parameters, an accuracy improvement in the results is noticeably observed, especially when compared with the results of alternative approaches applied in the same database.