Predictive Modeling of Yoga's Impact on Individuals with Venous Chronic Cerebrospinal System

Abstract

People leading a modern lifestyle often experience varicose veins, commonly attributed to factors associated with work and diet, such as prolonged periods of standing or excess weight. These disorders include elevated blood pressure in the lower extremities, especially the legs. An often-researched metric associated with these illnesses is the Vascular Clinical Severity Score (VCSS), which is connected to discomfort and skin discolorations. However, yoga appears to be a viable way to prevent and manage these problems, significantly lessening the negative consequences of varicose veins. The investigation of yoga's effect on VCSS in this study uses a novel strategy combining machine learning with the Extra Tree Classification (ETC), which is improved by the Cheetah Optimizer (CO) and Black Widow Optimizer (BWO). In this study, the ETC model was combined with previously mentioned optimizers, and two models were amalgamated, referred to as ETBW and ETCO. Through the evaluation of the performance of these models, it was discerned that the accuracy measure for prediction was associated with the ETCO model in the context of VCSS. By revealing subtle correlations between yoga treatments and VCSS results, this multidisciplinary approach seeks to provide a thorough knowledge of preventative and control processes. This research advances the understanding of vascular health by correlating yoga interventions with VCSS outcomes using machine learning and optimization algorithms. By enhancing predictive accuracy, it promotes multidisciplinary collaboration, personalized medicine, and innovation in healthcare, promising improved patient care and outcomes in varicose vein management.