Applied picture fuzzy sets with knowledge reasoning and linguistics in clinical decision support system

Motivation: Healthcare systems globally face significant resource and financial challenges. Moreover, these challenges have resulted in an existential paradigm shift driven by: (i) the growth in the demand for healthcare services is exacerbated by a global population characterised by an ageing demographic with increasingly complex healthcare needs, and (ii) rapid developments in healthcare technologies and drug therapies which can be seen in the new and emerging treatment options. A potential solution to address [or at least mitigate] these challenges is ‘telemedicine’ with nurse-led ‘triage’ systems; however, a limiting factor for ‘telemedicine’ is the management of imprecision and uncertainty in the diagnostic process. Contribution: In this paper we introduce a novel rule-based approach predicated on picture fuzzy sets to enable intelligent clinical decision support system which builds on previous research to create an approach predicated on picture fuzzy sets. Our principal contribution lies in the use of expert clinician preferences in a rule-based system which implements knowledge reasoning along with linguistic information to improve the diagnostic performance. Results: In ‘real-world’ case studies (using ethically approved anonymised patient data) we have investigated heart conditions, kidney stones, and kidney infections. Reported results for the proposed approach demonstrate a high level of accuracy in clinical diagnostic accuracy terms with reported accuracy in the range [92% to 95%] and a high confidence level when compared to alternative diagnostic matching methods.