Intelligent Medical Decision Support System Based on Imperfect Information

Dyczkowski, Krzysztof

doi:10.1007/978-3-319-67005-8

Cited by 22 publications

(12 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many conversion functions are available for the FCM. When the state values are in the range [-1, 1], it is necessary to use the hyperbolic tangent function (11):…”

Section: Figure 2 Msns Structurementioning

confidence: 99%

“…In the study of complex systems, as a rule, it is not possible to build a reliable mathematical model because of the large uncertainty in the interaction of system elements. Consequently, approaches using the application of specially developed decision support mechanisms based on elements of the fuzzy set theory, algebra of logic, semantic networks, the theory of cognitive analysis, and others were developed [10][11][12]. One method of modeling an uncertain and dynamic environment is the cognitive approach.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A Fuzzy Approach to the Synthesis of Cognitive Maps for Modeling Decision Making in Complex Systems

et al. 2022

View full text Add to dashboard Cite

The object of this study is fuzzy cognitive modeling as a means of studying semistructured socio-economic systems. The features of constructing cognitive maps, providing the ability to choose management decisions in complex semistructured socio-economic systems, are described. It is shown that further improvement of technologies necessary for developing decision support systems and their practical use is still relevant. This work aimed to improve the accuracy of cognitive modeling of semistructured systems based on a fuzzy cognitive map of structuring nonformalized situations (MSNS) with the evaluation of root-mean-square error (RMSE) and mean average squared error (MASE) coefficients. In order to achieve the goal, the following main methods were used: systems analysis methods, fuzzy logic and fuzzy sets theory postulates, theory of integral wavelet transform, correlation and autocorrelation analyses. As a result, a new methodology for constructing MSNS was proposed—a map of structuring nonformalized situations that combines the positive properties of previous fuzzy cognitive maps. The solution of modeling problems based on this methodology should increase the reliability and quality of analysis and modeling of semistructured systems and processes under uncertainty. The analysis using open datasets proved that compared to the classical ARIMA, SVR, MLP, and Fuzzy time series models, our proposed model provides better performance in terms of MASE and RMSE metrics, which confirms its advantage. Thus, it is advisable to use our proposed algorithm in the future as a mathematical basis for developing software tools for the analysis and modeling of problems in semistructured systems and processes. Doi: 10.28991/ESJ-2022-06-02-012 Full Text: PDF

show abstract

“…Many conversion functions are available for the FCM. When the state values are in the range [-1, 1], it is necessary to use the hyperbolic tangent function (11):…”

Section: Figure 2 Msns Structurementioning

confidence: 99%

mentioning

confidence: 99%

A Fuzzy Approach to the Synthesis of Cognitive Maps for Modeling Decision Making in Complex Systems

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Interval-valued aggregation functions are a new class of aggregation functions that have been developed recently by several researchers and successfully applied in decision making problems [19], [20]. For example, in [19], different intervalvalued aggregation functions are applied to design a fuzzy support system in medical diagnosis.…”

Section: Related Workmentioning

confidence: 99%

An interval-based aggregation approach based on Bagging and Interval Agreement Approach in ensemble learning

Maadi

Aickelin

Khorshidi

2020

2020 IEEE Symposium Series on Computational Intelligence (SSCI)

View full text Add to dashboard Cite

The main aim in ensemble learning is using multiple classifiers' outputs rather than one classifier output to aggregate them for more accurate classification. Generating an ensemble classifier generally is composed of three steps: selecting the base classifier, applying a sampling strategy to generate different individual classifiers and aggregation the classifiers' outputs. This paper focuses on the classifiers' outputs aggregation step and presents a new interval-based aggregation modeling using bagging resampling approach and Interval Agreement Approach (IAA) in ensemble learning. IAA is an interesting and practical aggregation approach in decision making which was introduced to combine decision makers' opinions when they present their opinions by intervals. In this paper, in addition to presenting a new aggregation approach in ensemble learning, we design some experiments to encourage researchers to use interval modeling in ensemble learning because it preserves more uncertainty and this leads to more accurate classification. For this purpose, we compared the results of implementing the proposed method to the majority vote, as the most commonly used and successful aggregation function in the literature, for 10 medical data sets. The results show the better performance of the interval modeling and the proposed interval-based aggregation approach in binary classification when it comes to ensemble learning. The Bayesian signed-rank test confirms the competency of our proposed approach.

show abstract

“…Aggregation functions are useful in many application areas 13 . In this contribution we will use interval‐valued aggregation functions which were developed by several authors and applied with a success 13–18 . Here we present the results on aggregation methods connected with possible and necessary aggregation functions 19 .…”

Section: Introductionmentioning

confidence: 99%

“…13 In this contribution we will use interval-valued aggregation functions which were developed by several authors and applied with a success. [13][14][15][16][17][18] Here we present the results on aggregation methods connected with possible and necessary aggregation functions. 19 The first approach to use these functions and interval modelling in the case of large number of attributes for microarrays was presented in Bentkowska 16 (Chapter 5), where the aim was to find the best classifiers with respect to the proposed there measure based on accuracy at the specific points on the receiver operating characteristic (ROC) curve.…”

mentioning

confidence: 99%

Interval modelling in optimization of k‐NN classifiers for large number of attributes in data sets on an example of DNA microarrays

Bentkowska

Bazan

Zaręba

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

In this contribution there are considered interval‐valued methods of improving the quality of classification by the k‐ N N binary classifiers in the case of large number of attributes in data sets. One of the possible applications of the introduced method are the microarray data so the presented results may be applied in medical diagnosis support, for example, in identification of marker genes. The proposed algorithm involving reduction methods of time complexity and interval modeling of data using interval‐valued aggregation functions has a significantly higher classification quality than the other considered algorithm with the aggregation method involving the numerical arithmetic mean. The former algorithm is based on the interval‐valued aggregation of uncertainty intervals determined on the basis of certainty coefficients of individual k‐ N N classifiers while the latter is based on the aggregation of certainty coefficients of individual k‐ N N classifiers with the use of the numerical arithmetic mean. Moreover, the performance of the new algorithm based on interval‐valued methods was compared with the known from the literature classifiers in cancer diagnosis based on gene expression microarrays. The obtained results prove that in practical applications the newly proposed algorithm can be successfully used.

show abstract

Intelligent Medical Decision Support System Based on Imperfect Information

Cited by 22 publications

References 0 publications

A Fuzzy Approach to the Synthesis of Cognitive Maps for Modeling Decision Making in Complex Systems

A Fuzzy Approach to the Synthesis of Cognitive Maps for Modeling Decision Making in Complex Systems

An interval-based aggregation approach based on Bagging and Interval Agreement Approach in ensemble learning

Interval modelling in optimization of k‐NN classifiers for large number of attributes in data sets on an example of DNA microarrays

Contact Info

Product

Resources

About