Application of rough sets in the presumptive diagnosis of urinary system diseases

Czerniak, Jacek M.; Zarzycki, Hubert

doi:10.1007/978-1-4419-9226-0_5

Cited by 80 publications

(41 citation statements)

References 2 publications

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“…In our earlier study, focusing on the impact of precision of numerical parameters on the quality of Bayesian network results [5], we selected six medical data sets from the Irvine Machine Learning Repository: Acute inflammation [9], SPECT Heart, Cardiotocography, Hepatitis, Lymphography [10], and Primary Tumor [10]. We used the following two selection criteria: (1) the data set had to have at least one disorder variable and (2) it should not contain too many missing values and too many continuous variables.…”

Section: Models Studied and Model Quality Criterionmentioning

confidence: 99%

Impact of Bayesian Network Model Structure on the Accuracy of Medical Diagnostic Systems

Oniśko

Drużdżel

2014

Artificial Intelligence and Soft Computing

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Abstract.While Bayesian network models may contain a handful of numerical parameters that are important for their quality, several empirical studies have confirmed that overall precision of their probabilities is not crucial. In this paper, we study the impact of the structure of a Bayesian network on the precision of medical diagnostic systems. We show that also the structure is not that important -diagnostic accuracy of several medical diagnostic models changes minimally when we subject their structures to such transformations as arc removal and arc reversal.

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Section: Models Studied and Model Quality Criterionmentioning

confidence: 99%

Impact of Bayesian Network Model Structure on the Accuracy of Medical Diagnostic Systems

Oniśko

Drużdżel

2014

Artificial Intelligence and Soft Computing

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“…Among them, it is impossible not to recall that several fuzzy number comparison methods and indices have been researched since 1977 by Zadeh [12], Yager [10,11], Kaufman [14,15], Chang [5], and Amado [1]. Bortolan and Degani [5] and Dadgostar [1] reviewed some of the methods for ranking fuzzy sets, including Yager's first, second, and third indexes, Chang's algorithm, Adamo's method, Baas and Kwakernaak's method [2], Baldwin and Guild's method [3], Kerre's method [9], Jain's method [7,8], and Dubois and Prade's four grades [6] of dominance (PD, PSD, ND, NSD). Dadgostar and Kerr [1] proposed a consistent method, called the partial comparison method (PCM).…”

Section: Introductionmentioning

confidence: 99%

Comparing Fuzzy Numbers Using Defuzzificators on OFN Shapes

Czerniak

Dobrosielski

Filipowicz

2017

Studies in Fuzziness and Soft Computing

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This chapter concerns an issue of comparing fuzzy numbers. The relationship of similarity is probably the most widely used and most difficult to determine the measure of compliance precisely. Analysis of the similarity between two objects is an essential tool in biology, taxonomy, and psychology, and is the basis for reasoning by analogy. This chapter describes methods for determining the similarity used in fuzzy logic. Many of them were dedicated only to triangular or trapezoidal fuzzy numbers. This was a computing inconvenience and raised the question about the axiological basis for such comparisons. The authors have proposed two new approaches to comparing fuzzy numbers using one of the known fuzzy number extensions that are Ordered Fuzzy Numbers (OFNs). This has allowed us to simplify operations and eliminate said dualism. Two order-sensitive defuzzification methods are presented in the chapter. For OFN numbers with positive order (compliant with the direction of the OX axis increase) the results of defuzzifications are results for numbers of different notations, for example, L-R, whereas for numbers with negative orders, the defuzzification result changes. An important part of the chapter is a catalogue of the shapes of numbers in OFN notation. This is probably the first summary of basic shapes of those numbers with the results of defuzzifications using several methods.

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“…Trends identified in the sequence of literals are then used to develop trend prediction rules. Therefore fuzzy logic [12,13,16,35] was used to develop linguistic data input. Data for the study were quotations of the Nasdaq Composite index from the years 2006-2016.…”

Section: Introductionmentioning

confidence: 99%

Detecting Nasdaq Composite Index Trends with OFNs

Zarzycki

Czerniak

Dobrosielski

2017

Studies in Fuzziness and Soft Computing

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The chapter presents a novel way of describing changes in the stock index and the identification of potential trends. The authors already used a similar approach to describe the stock exchange index [16]; this chapter is a continuation and another application of work on this issue. The method for detecting patterns in a trend by means of linguistic variables is described. The use of computational operations on numbers in the Ordered Fuzzy Number (OFN) notation [40][41][42] enables us to set the values of linguistic variables and thus conduct fuzzification of the input. By using one OFN number it is possible to store five parameters of index quotations (open, high, low, and close values as well as a change direction). The OFN numbers are conveyed into a linguistic form. In order to find trend sequence similarity the following applies: sequence identity with the input frame expressed as a percentage, frame size, the level of threshold conformity with the frame (threshold), and how often the pattern is present (frequency). A dedicated computer program to detect patterns is implemented. The program used data from the index Nasdaq Composite from the years 2006-2016. The results represent a further step to develop effective methods of rule-based forecasting.

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Application of rough sets in the presumptive diagnosis of urinary system diseases

Cited by 80 publications

References 2 publications

Impact of Bayesian Network Model Structure on the Accuracy of Medical Diagnostic Systems

Impact of Bayesian Network Model Structure on the Accuracy of Medical Diagnostic Systems

Comparing Fuzzy Numbers Using Defuzzificators on OFN Shapes

Detecting Nasdaq Composite Index Trends with OFNs

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