Medical diagnosis support using uncertainty and imprecision measures

Straszecka, Ewa; Straszecka, Joanna

doi:10.1109/icsmc.2004.1401126

Cited by 7 publications

(7 citation statements)

References 4 publications

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“…(Asuncion & Newman, 2007). The bpa without combination also failed for a database that originated from research (Straszecka et al , 1999; Straszecka & Straszecka, 2004). Therefore, reasons for these malfunctions and a development of the algorithm have been investigated.…”

Section: Tests and Resultsmentioning

confidence: 99%

“…Simultaneously, the is calculated. The η T (8) is assumed as the minimal threshold value that still allows for maximum possible (Straszecka & Straszecka, 2004). The belief value for the threshold is .…”

Section: Methods Of Symptom Representation and Knowledge Combiningmentioning

confidence: 99%

“…When several hypotheses have to be analysed during an inference, their belief measures can be compared. They should be found for maximal plausibility of the hypotheses that guarantees that all possible information has been used to find a conclusion (Straszecka & Straszecka, 2004). Thus, the hypothesis with the greatest Bel value is the final conclusion.…”

Section: A Model Of Medical Diagnosis In the Dempster–shafer Theorymentioning

confidence: 99%

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Combining knowledge from different sources

Straszecka¹

2010

Expert Systems

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The present paper deals with the problem of an assessment of symptoms in medical diagnosis. A unified interpretation of symptoms is necessary to estimate their significance in a diagnosis. Yet, even if they are properly defined, different evaluations of them based on experts' knowledge or statistical estimation are possible. The present study aims at combining evaluations that may originate from an expert or can be found from statistical features of the data, as well as those determined for 'easy' and 'difficult' diagnostic cases. A model of diagnostic inference is proposed in the framework of the Dempster-Shafer theory extended for fuzzy focal elements. The basic probability assignment defined in this theory estimates weights of symptoms. Two basic probability assignments can be created and then combined. In this way weights of symptoms represent knowledge common for two kinds of data or obtained from an expert and from data. Thus, a combination of heuristics and data mining results becomes possible. An algorithm of the basic probability assignment calculation is suggested and tested for medical data: a database from the internet and individually gathered data.

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Section: Tests and Resultsmentioning

confidence: 99%

Section: Methods Of Symptom Representation and Knowledge Combiningmentioning

confidence: 99%

Section: A Model Of Medical Diagnosis In the Dempster–shafer Theorymentioning

confidence: 99%

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Combining knowledge from different sources

Straszecka¹

2010

Expert Systems

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“…The theoretical background of the BPA calculation is given in [29]. Here only a brief summary of the algorithm is mentioned with an introduction of notations.…”

Section: The Dempster-shafer Theory In the Diagnosis Supportmentioning

confidence: 99%

“…The imprecision measure can be the membership function of the fuzzy set. This approach is introduced in [29,30] and shares Bezdek's conviction [2] that probability is something different from a membership and each of the measures should be individually interpreted. The method is convenient for applications, particularly when the diagnosis support requires combining data driven and human expert knowledge.…”

Section: Introductionmentioning

confidence: 99%

Membership Functions for Fuzzy Focal Elements

Porębski¹,

Straszecka²

2016

Archives of Control Sciences

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The paper presents a study on data-driven diagnostic rules, which are easy to interpret by human experts. To this end, the Dempster-Shafer theory extended for fuzzy focal elements is used. Premises of the rules (fuzzy focal elements) are provided by membership functions which shapes are changing according to input symptoms. The main aim of the present study is to evaluate common membership function shapes and to introduce a rule elimination algorithm. Proposed methods are first illustrated with the popular Iris data set. Next experiments with five medical benchmark databases are performed. Results of the experiments show that various membership function shapes provide different inference efficiency but the extracted rule sets are close to each other. Thus indications for determining rules with possible heuristic interpretation can be formulated.

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Rule Extraction Using the Dempster-Shafer Theory in the Medical Diagnosis Support

Porębski¹,

Straszecka²

2016

2016 Third European Network Intelligence Conference (ENIC)

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Medical diagnosis support using uncertainty and imprecision measures

Cited by 7 publications

References 4 publications

Combining knowledge from different sources

Combining knowledge from different sources

Membership Functions for Fuzzy Focal Elements

Rule Extraction Using the Dempster-Shafer Theory in the Medical Diagnosis Support

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