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A Case-Based Decision Support System for Individual Stress Diagnosis Using Fuzzy Similarity Matching
Mälardalen University, School of Innovation, Design and Engineering. (Intelligent Systems)ORCID iD: 0000-0002-1212-7637
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Sustainable Development of Society and Technology.ORCID iD: 0000-0002-5562-1424
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0001-9857-4317
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2009 (English)In: Computational intelligence, ISSN 0824-7935, E-ISSN 1467-8640, Vol. 25, no 3, p. 180-195Article in journal (Refereed) Published
Abstract [en]

Stress diagnosis based on finger temperature signals is receiving increasing interest in the psycho-physiological domain. However, in practice, it is difficult and tedious for a clinician and particularly less experienced clinicians to understand, interpret and analyze complex, lengthy sequential measurements in order to make a diagnosis and treatment plan. The paper presents a case-based decision support system to assist clinicians in performing such tasks. Case-based reasoning is applied as the main methodology to facilitate experience reuse and decision explanation by retrieving previous similar temperature profiles. Further fuzzy techniques are also employed and incorporated into the case-based reasoning system to handle vagueness, uncertainty inherently existing in clinicians reasoning as well as imprecision of feature values. Thirty nine time series from 24 patients have been used to evaluate the approach (matching algorithms) and an expert has ranked and estimated similarity. On average goodness-of-fit for the fuzzy matching algorithm is 90% in ranking and 81% in similarity estimation which shows a level of performance close to an experienced expert. Therefore, we have suggested that a fuzzy matching algorithm in combination with case-based reasoning is a valuable approach in domains where the fuzzy matching model similarity and case preference is consistent with the views of domain expert. This combination is also valuable where domain experts are aware that the crisp values they use have a possibility distribution that can be estimated by the expert and is used when experienced experts reason about similarity. This is the case in the psycho-physiological domain and experienced experts can estimate this distribution of feature values and use them in their reasoning and explanation process.

Place, publisher, year, edition, pages
Blackwell Publishing , 2009. Vol. 25, no 3, p. 180-195
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-12355DOI: 10.1111/j.1467-8640.2009.00337.xISI: 000268099800003Scopus ID: 2-s2.0-67651202655OAI: oai:DiVA.org:mdh-12355DiVA, id: diva2:419849
Available from: 2011-05-30 Created: 2011-05-30 Last updated: 2017-12-11Bibliographically approved

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Begum, ShahinaAhmed, Mobyen UddinFunk, PeterXiong, Ning

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