Interpretation of Medical Symptoms Using Fuzzy Focal Elements

Straszecka, Ewa; Straszecka, Joanna

doi:10.1007/3-540-32390-2_33

Cited by 5 publications

(6 citation statements)

References 1 publication

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“…The imprecision of the premise is separately represented by the membership functions of the fuzzy sets A j membership function describes one condition in (1). The imprecision of the whole premise can be determined according to possibility theory as the matching level [30]: η…”

Section: The Dempster-shafer Theory In the Diagnosis Supportmentioning

confidence: 99%

“…The belief (Bel) and plausibility (Pl) measures described in the Dempster-Shafer theory [22] are also useful for evaluation of a diagnosis [30]. Particularly the Bel value can be used to choose the best supported diagnosis among l = 1, ..., C diagnostic hypotheses for the investigated x x x data case.…”

Section: The Dempster-shafer Theory In the Diagnosis Supportmentioning

confidence: 99%

“…where the α parameter (α ∈ (−1, 1)) provides different gradient of the trapezoid slopes while maintaining the condition of a half membership value for intersection points x cross point, a correction of points location by making functionŠs slopes almost vertical is necessary [30]:…”

Section: Triangular and Trapezoidal Membership Functionsmentioning

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%

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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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Section: The Dempster-shafer Theory In the Diagnosis Supportmentioning

confidence: 99%

Section: The Dempster-shafer Theory In the Diagnosis Supportmentioning

confidence: 99%

Section: Triangular and Trapezoidal Membership Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Membership Functions for Fuzzy Focal Elements

Porębski¹,

Straszecka²

2016

Archives of Control Sciences

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“…The description of the disease D usually involves a set of symptoms D s = {s 1 · · · s M }, being characteristic patterns of M selected parameters [9]. Their coincidence is defined as a set of conditions C D {M }, also called disease templates allowing the doctor to make evidence of certain pathology.…”

Section: Disease-domain Representation Of Casesmentioning

confidence: 99%

Clinical Examples as Non-uniform Learning and Testing Sets

Augustyniak

2010

Artificial Intelligence and Soft Computing

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Abstract.Clinical examples are widely used as learning and testing sets for newly proposed artificial intelligence-based classifiers of signals and images in medicine. The results obtained from testing are usually taken as an estimate of the behavior of automatic recognition system in presence of unknown input in the future. This paper investigates and discusses the consequences of the non-uniform representation of the medical knowledge in such clinically-derived experimental sets. Additional challenges come from the nonlinear representation of the patient status in particular parameters' domain and from the uncertainty of the reference provided usually by human experts. The presented solution consists of representation of all available cases in multidimensional diagnostic parameters or patient status spaces. This provides the option for independent linearization of selected dimensions. The recruitment to the learning set is then based on the case-to-case distance as selection criterion. In result, the classifier may be trained and tested in a more suitable way to cope with unpredicted patterns.

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Diagnostic Rule Extraction Using the Dempster-Shafer Theory Extended for Fuzzy Focal Elements

Porębski

Straszecka

2017

Advances in Intelligent Systems and Computing

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Interpretation of Medical Symptoms Using Fuzzy Focal Elements

Cited by 5 publications

References 1 publication

Membership Functions for Fuzzy Focal Elements

Membership Functions for Fuzzy Focal Elements

Clinical Examples as Non-uniform Learning and Testing Sets

Diagnostic Rule Extraction Using the Dempster-Shafer Theory Extended for Fuzzy Focal Elements

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