RETRACTED ARTICLE: A fuzzy-based adaptive multi-input–output scheme in lieu of diabetic and hypertension management for post-operative patients: an human–machine interface approach with its continuum

Abstract

Here, a multi-input–output control strategy-based adaptive system is implemented to regulate the glycaemic and hypertension (HT) level concurrently for post-operative patients. The current medical research identifies the relationship between diabetes and HT, and these two diseases have possible overlap in their disease aetiology. Based on the continuum, the blood glucose and blood pressure (BP) have to be measured independently and control the infusion to maintain optimally based on the variations of diabetic mellitus and HT to avoid the major complications for the perioperative condition. Based on the analysis, the post-operative strain may increase HT and may increase the glycaemic level, and this uncertainty of disparity may lead to BP, hyperinsulinemia, cardio diseases, and osmotic diuresis along with hyperglycaemia. The proposed adaptive cascade control strategy illustrates two different types of control loops independently, and these loops adopt an adaptive control strategy with parametric compensation. This adaptive control algorithm integrates the cascade methodology along with expert knowledge to treat these diseases by using adaptation laws with the help of fuzzy logic to regulate the proper insulin and sodium nitroprusside (SNP) infusion. The output response of the plasma glucose concentration and HT regulation was shown along with insulin infusion rate and SNP infusion rate based on the extensive simulation readings. The attained simulation results demonstrate that the adaptive control strategy shows better outcomes for the infusion and may achieve potentially better control on HT and glycaemic levels for post-operative patients.