Application of uncertainty-aware similarity measure to classification in medical diagnosis

The uncertainty is a common problem in many areas of science. The best-studied variant is incomplete data where the values of some attributes are unknown. It turns out that even in this situation, many known classification methods fail. The objective of the work is to develop an effective classification method for uncertain data including pre-processing and uncertainty aware similarity-based algorithms. The problem of uncertainty is particularly important in medical diagnostics where incompleteness and uncertainty is often a natural and permanent feature of the data. The paper presents the evaluation of the proposed methods on two publicly available data sets and compare the effectiveness of the previous results.