Ayurvedic Elixirs for Digital Generation Using Machine Learning

To improve individualized healthcare, this study proposes an innovative Ayurvedic medication recommendation system that makes use of a knowledge graph structure. The system uses a knowledge graph to describe the complex connections between ailments and their accompanying medications, giving a thorough depiction of Ayurvedic therapeutic concepts. Users provide their exact disease as well as important details including age and pertinent health issues, such as diabetes, as well as their unique sickness. After that, the algorithm deftly eliminates medications that are incompatible with the specified demographic data, resulting in a personalized and secure suggestion. This system's use of the Hyperlink-Induced Topic Search (HITS) algorithm sets it apart from others. By carefully incorporating HITS, drugs are awarded authority and hub scores inside the knowledge graph, allowing the system to identify the most authoritative and influential treatments for a certain condition. To provide a tailored and effective suggestion process, medications are then offered to the user in a prioritized manner depending on their rankings. In addition to streamlining the selection process, this innovative method for recommending Ayurvedic medications also makes sure that the medicines match the user's unique health profile. Our approach provides a paradigm change in Ayurvedic healthcare by combining the strength of knowledge graphs with the accuracy of the HITS algorithm, opening the path for more efficient and customized treatment plans.