Forecasting the Yoga Influence on Chronic Venous Insufficiency: Employing Machine Learning Methods

This investigation introduces a groundbreaking approach to unravel the complexities of Chronic Venous Insufficiency (CVI) by leveraging machine learning, notably the Support Vector Classification (SVC), alongside optimization systems like Dwarf Mon-goose Optimization (DMO) and Smell Agent Optimization (SAO). This pioneering strategy not only aims to bolster predictive Precision but also seeks to optimize personalized treatment paradigms for CVI, presenting a compelling avenue for the advancement of healthcare solutions. The study aims to predict the impact of yoga on CVI using a comprehensive dataset, incorporating demographic information, baseline severity indicators, and yoga practice details. Through meticulous feature engineering, machine learning algorithms forecast outcomes such as changes in symptom severity and overall well-being improvements. This predictive model has the potential to transform personalized CVI treatment plans by offering tailored recommendations for specific yoga practices, optimizing therapeutic approaches, and guiding efficient healthcare resource allocation. Ethical considerations, patient preferences, and safety are highlighted for responsible translation into clinical settings. The integration of SVC with optimization systems presents a novel and promising approach, contributing meaningfully to personalized CVI management and providing valuable insights for current and future practices. The results obtained for VCSS-PRE and VCSS-1 unequivocally highlight the outstanding performance of the SVDM model in both prediction and categorization. The model achieved remarkable Accuracy and Precision values, attaining 92.9% and 93.1% for VCSS-PRE and 94.3% and 94.9% for VCSS-1.