Data-driven multiple criteria decision making for diagnosis of thyroid cancer

Chao Fu (), Weiyong Liu and Wenjun Chang
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Chao Fu: Hefei University of Technology
Weiyong Liu: Anhui Medical University
Wenjun Chang: Hefei University of Technology

Annals of Operations Research, 2020, vol. 293, issue 2, No 17, 833-862

Abstract: Abstract In the era of the Internet and big data, data permeate the entire process of multiple criteria decision making (MCDM). Therefore, generation of rational solutions from current observations and historical data has become an important and interesting issue. To address this issue, this paper proposes a data-driven MCDM framework in the context of the evidential reasoning approach. Three challenges in the framework are met, including the transformation of observations into assessments, the learning of parameters and their constraints from historical data, and the generation of a data-driven solution. The proposed framework is then used to model the diagnosis of thyroid cancer and generate data-driven diagnostic results. The three challenges in the application are met to aid radiologists in improving the diagnostic accuracy of thyroid cancers. To examine whether the application of the proposed data-driven MCDM framework to the diagnosis of thyroid cancer can help improve diagnostic accuracy, we conduct a case study by using the examination reports of three radiologists from July 2015 to October 2017 in the ultrasonic department of a tertiary hospital located in Hefei, Anhui Province, China.

Keywords: Multiple criteria decision making; Data-driven decision; Learning of weights; Evidential reasoning approach; Diagnosis of thyroid cancer (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10479-018-3093-7

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