Abstract

Abstract

Accurate disease identification is a critical aspect of healthcare, ensuring effective treatment and management. Integrating traditional methods like Rognidan and Vikriti Vigyan with modern diagnostic techniques can enhance the precision of diagnoses and bridge the gap between ancient wisdom and contemporary medical advancements. The challenge in modern healthcare lies in the accurate diagnosis of diseases, which is often hindered by the limitations of conventional diagnostic methods. This study aims to highlight the complementary strengths of traditional and contemporary diagnostic systems in improving healthcare outcomes. To enhance disease identification, a hybrid approach integrating Ayurvedic and modern diagnostic techniques is employed. Patients with doshic imbalances or chronic illnesses are ethically selected with informed consent. Traditional methods such as Rognidan, Vikriti Vigyan, Nadi Pariksha, and tongue examination are combined with modern diagnostics like blood tests, imaging (X-ray, MRI), and molecular assays. Data is pre-processed by removing inconsistencies, standardizing formats, and applying Trust-Aware Multi-Criteria Collaborative Filtering (TAMCCF) to ensure data reliability. Advanced biomarker analysis using genetic, proteomic, and metabolic markers, supported by Causal Inference with Hemogram Markers (CIHM), helps in early disease detection. Real-time monitoring through wearable devices enhances patient tracking, while the TBGAT method leverages graph attention networks to uncover complex clinical patterns, significantly improving diagnostic accuracy.