Background The projection studies are imperative to satisfy demands for health care systems and proper response to the public health problems such as inflammatory bowel disease (IBD). Methods To accomplish this, we established an illness-death model based on available data to project the future prevalence of IBD in Asia, Iran in particular, separately from 2017 to 2035. We applied two deterministic and stochastic approaches. Results In 2035, as compared to 2020, we expected a 2.5-fold rise in prevalence for Iran with 69 thousand cases, a 2.3-fold increment for North Africa and the Middle East with 220 thousand cases, quadrupling of the prevalence for India with 2.2 million cases, a 1.5-fold increase for East Asia region with 4.5 million cases, and a 1.6-fold elevation in prevalence for high‐income Asia‐Pacific and Southeast Asia regions with 183 and 199 thousand cases respectively. Conclusions Our results showed an emerging epidemic for the prevalence of IBD in Asia regions and/or countries. Hence, we suggest the need for immediate action to control this increasing trend in Asia and Iran. However, we were virtually unable to use information about age groups, gender, and other factors influencing the evolution of IBD in our model due to lack of access to reliable data.
The analysis of anthropometric studies of modern youth was performed. Cluster analysis and sequential Wald analysis were used for building an expert system to study the gender distribution of anthropometric indicators.
We compare cluster analysis, Wald's sequential analysis, and the method of coordinate system rotation for the classification of patients with acute destructive pancreatitis and patients with acute combined radiation damage for early detection of complications. In the first case, the Wald analysis is efficient. In the second case, k-means cluster analysis and coordinate rotation method are proposed.
The method of building expert systems for medical prediction of severity in patients is purposed. The method is based on using Voronoi diagrams. Examples of using the method are described in the paper.
This paper introduces the concept of e-separability. Necessary and sufficient conditions of e-separability are proved. It is proved that the problem of e-separability of two sets can be reduced to the trivial problem of separability of their disjoint e-nets. PROBLEM STATEMENT Let two finite setsA R d Ì and B R d Ì be given, and let their cardinalities be | | A n A = and | | B n B = . Assume that A B Ë conv and that B A Ë conv . In the simplest case when the convex envelopes of the sets A R d Ì and B R d Ì aredisjoint, they can be separated, i.e., a hyperplane can be found such that these sets will be located on the opposite sides of this hyperplane. Assume that the sets are inseparable, i.e., conv conv¹AE . The following question arises: when can these sets be separated by eliminating a small number of points from them, for example, e Î ( , ) 0 1 parts of their total number? At present, there exist many classification methods each of which has advantages and drawbacks. The Fischer discriminant analysis [1] is most popular. It is widely used in informatics branches such as machine learning, information search, and pattern recognition. The complexity of the algorithm of linear discriminant analysis is estimated to be O ndt t ( ) + 3 , where n is the number of observations in a training set, d is the number of features, and t n d = min( , ) [2]. Therefore, it is impossible to use the algorithm when the values of n and d are large.The Bayesian classifier [3] is optimal, it is easily implemented in software, and many classification methods are constructed on its basis. However, since, in practice, likelyhood functions of classes are reconstructed from finite data samples, the Bayesian classifier ceases to be optimal [4]. Its algorithmic complexity is estimated to be O nd ( ) [5]. A comparatively new support vector method well known in the literature as SVM [6] leads the maximization of the width of the separating band between classes owing to the optimal separating hyperplane principle. Thus, this method promotes a more reliable classification. However, at the same time, it is not resistant to noise in initial data. An essential drawback of the method is the absence of developed general methods for constructing straightening spaces and kernels that are most relevant to a concrete problem [7]. The complexity of the algorithm of the support vector method is estimated to be O n ( ) 3 [8]. The classification with the help of cluster analysis and also Wald's sequential analysis with the use of Kulbak's information measure are described in [9]. BASIC DEFINITIONS Definition 1. Sets A and B are called e-separable if there are A A 1 Ì and B B
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