ABSTRACT
Modern technological advancements in the healthcare sector have led to the massive growth in improved medical facilities that are essentially accessible to the critical patients during emergency situations. In this chapter, we propose a novel, proficient, and cost-effective health system model based on the fuzzy logic design architecture in which several attributes related to the generic human physical conditions are employed to assess their synchronous impact on heart patients suffering simultaneously from the prevalent universal pandemic disease. The fuzzy inputs employed in the presented smart medical systems include the blood oxygen level, body temperature, and age parameters for determining the COVID-19 disease plausibility factor. Moreover, the serum triglycerides, systolic blood pressure, and age input attributes are used to evaluate the fuzzy output variable of cardiovascular disease risk. These diverse parameters with the implicit uncertainties in practical healthcare scenarios are conveniently characterized by the fuzzy linguistic information. Both the contemplated widespread endemics are jointly modeled and implemented through the multiple-input multiple-output (MIMO) fuzzy inference scheme. This integrated intelligent design for smart health monitoring is typically effective for cardiac patients diagnosed with the current ongoing COVID-19 symptoms. Through the rigorous simulation experiments and analysis, the developed adaptive optimization model is assessed in terms of the information-theoretic metrics and accuracy measures for considerable comparisons with the standard technique. This is particularly significant for the decisive determination of the connected risk level associated with the aforementioned heart circulatory failure and infectious disease outbreaks nowadays being examined. A sample dataset medical record of specific patients is employed for the effective estimation of the fuzzy illness-related output variables and subsequent validation of the proposed fuzzy multiattribute decision-making framework. The simulation results demonstrate the enhanced performance of the proposed autonomic medical system implemented with the multivariate fuzzy logic controller modeling the distinctive physiological conditions.
