Similarity and Agreement Measures and Their Application in Medical Diagnostic Prediction System
| Autor: | Mohammad Al-Kaseasbeh, Malek Alksasbeh |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Computer science Context (language use) 02 engineering and technology Similarity measure computer.software_genre information systems Set (abstract data type) 03 medical and health sciences 0302 clinical medicine Similarity (network science) Agreement measure 0202 electrical engineering electronic engineering information engineering Information system General Materials Science 030212 general & internal medicine Electrical and Electronic Engineering Kappa function Structure (mathematical logic) Measure (data warehouse) General Engineering diagnostic prediction soft expert set similarity measure Pattern recognition (psychology) 020201 artificial intelligence & image processing Data mining lcsh:Electrical engineering. Electronics. Nuclear engineering computer lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 8, Pp 228685-228692 (2020) |
| ISSN: | 2169-3536 |
| Popis: | Due to importance of measuring the degree of resemblance, the similarity measure is widely adopted in various areas of the information systems (e.g., medical informatics and information retrieval) and in several applications like medical diagnostic, image processing, and pattern recognition. However, most of the existing similarity measures focus mainly on the degree of similarity without consulting expert(s) about the results. In this paper, an efficient tool for measuring similarity and agreement of objects that embeds experts’ opinions is proposed to assess similarity among features and agreement of opinions among experts. To obtain such robust measuring tool, three construction steps were followed. Firstly, adapting soft expert set as a general structure that consists of four components: objects, attributes, experts, and experts’ opinions. Secondly, representing the soft expert set, without losing stored information, in such a way as to fit the proposed similarity-agreement measure and make it simpler and more meaningful than the similar existing measures. Thirdly, axiomatizing the similarity-agreement measure for the case of two experts to simplify the model. Further, a diagnostic prediction application and its algorithm is discussed in this context, along with analysis of the experimental results. Analysis of performance of the proposed similarity-agreement measure revealed that it has high accuracy, sensitivity, and value of the F-measure and that it has better performance than existing state-of-the-art tools. |
| Databáze: | OpenAIRE |
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