Affiliations: [a] Department of Computer Science, University of Uyo, Uyo, Akwa Ibom State, Nigeria | [b] Department of Industrial Production Engineering, National Institute of Engineering, Mysore, India | [c] Department of Information and Communication Technology, Mangosuthu University of Technology, Durban, South Africa
Correspondence:
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Corresponding author: Uduak Umoh, Department of Computer Science, University of Uyo, Uyo, PMB 1017, Uyo, Akwa Ibom State, Nigeria. %****␣his-17-his210005_temp.tex␣Line␣25␣**** E-mail: [email protected].
Abstract: Healthcare systems need to overcome the high mortality rate associated with cardiovascular disease and improve patients’ health by using decision support models that are both quantitative and qualitative. However, existing models emphasize mathematical procedures, which are only good for analyzing quantitative decision variables and have failed to consider several relevant qualitative decision variables which cannot be simply quantified. In solving this problem, some models such as interval type-2 fuzzy logic (IT2FL) and flower pollination algorithm (FPA) have been used in isolation. IT2FL is a simplified version of T2FL, with a reduced computation complexity and additional design degrees of freedom, but it cannot naturally achieve the rules it uses in making decisions. FPA is a bio-inspired method based on the process of pollination, executed by the flowering plants, with the ability to learn, generalize and process numerous measurable data, but it is not able to describe how it reaches its decisions. The hybrid intelligent IT2FL-FPA system can conquer the constraints of individual approaches and strengthens their robustness to cope with healthcare data. This work develops a hybrid intelligent telemedical monitoring and predictive system using IT2FL and FPA. The main objective of this paper is to find the best membership functions (MFs) parameters of the IT2FL for an optimal solution. The FPA technique is employed to find the optimal parameters of the MFs used for IT2FLSs. The authors tested two data sets for the monitoring and prediction problems, namely: cardiovascular disease patients’ clinical and real-time datasets for shock-level monitoring and prediction.
