A systematic clinical healthcare model for assessing post-pandemic recovery in heart diagnosis

Globally, the occurrence of cardiovascular disease is linked to atherosclerosis in heart vessels due to poor diet or underlying illnesses. Additionally, post-pandemic diseases like COrona VIrus Disease-19 (COVID-19) have accentuated cardiovascular complications, leading to significant fatalities among sufferers. The research addresses the crucial need for monitoring and preventative measures in individuals who have recovered from COVID-19, emphasizing the heightened cardiovascular risks associated with the human population. To safeguard individuals from heart diseases based on the decision-making framework of physicians, the study aimed to develop a clinical decision support system with a novel fuzzy approach. In decision analysis, membership grades alone are not sufficient to analyze objects in the universe. The addition of reference parameters provides freedom to decision makers in selecting these grades. Consequently, the complex spherical fuzzy N-soft set with an associated reference parameter offers a robust approach for modeling such uncertainties. For defuzzification, the Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) approach was used. To specify the superiority of weighing approaches based on group decision-making techniques, result validation is established using four distinct methods: equal weight, ROC weight, RS weight, and entropy weight. •To examine the impact of COVID-19 on heart disease diagnoses.•Adaptive fuzzy set frameworks are employed to represent clinical experts’ opinions.•Analyzed which stage of heart disease is increased in the post-pandemic period.•Complex spherical fuzzy N-soft rough set, elucidating the uncertain consequences.