Paper on an Agreement Ratio of Fuzzy Sets accepted to IEEE SSCI 2016

The paper "Measuring Agreement on Linguistic Expressions in Medical Treatment Scenarios" by Javier Navarro, Christian Wagner, Uwe Aickelin, Lynsey Green and Robert Ashford has been accepted to the 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016) which will be held in Athens, Greece in December 2016. This paper comes from a study made in collaboration with the East Midlands Sarcoma Service, Nottingham University Hospitals.

Abstract of the paper is included below. A full version of the paper will be made available after final amendments.

Abstract: Quality of life assessment represents a key process of deciding treatment success and viability. As such, patients' perceptions of their functional status and well-being are important inputs for impairment assessment. Given that patient completed questionnaires are often used to assess patient status and determine future treatment options, it is important to know the level of agreement of the words used by patients and different groups of medical professionals. In this paper, we propose a measure called the Agreement Ratio which provides a ratio of overall agreement when modelling words through Fuzzy Sets (FSs). The measure has been specifically designed for assessing this agreement in fuzzy sets which are generated from data such as patient responses. The measure relies on using the Jaccard Similarity Measure for comparing the different levels of agreement in the FSs generated. Synthetic examples are provided in order to show how to calculate the measure for given Fuzzy Sets. An application to real-world data is provided as well as a discussion about the results and the potential of the proposed measure.