MediMatch: AI-Driven Drug Recommendation System

Ayurveda, a traditional Indian medical system, emphasizes personalized treatment based on individual constitutions and imbalances. This paper explores the potential of machine learning (ML) for developing an intelligent Ayurvedic recommendation system. The system would leverage patient data, including Vikruti (imbalances) symptoms, and potentially lifestyle factors (severity). Machine learning algorithms, such as decision trees, cosine similarity, bagging, and random forest, would be trained on historical patient data linked to successful Ayurvedic interventions. Challenges include data collection, ensuring data quality, selecting the best algorithms, and clinical validation. Recent techniques like deep learning and transfer learning have shown promising results in medical applications. The proposed system aims to enhance personalization by detecting disease and recommending Ayurvedic medicines tailored to a user's unique body. It also aims to improve accessibility by providing a user-friendly platform for individuals to gain insights into their Ayurvedic health and explore potential remedies. Additionally, the system will support practitioners by offering data-driven recommendations to complement their expertise. This approach holds promise for bridging the gap between traditional Ayurvedic knowledge and modern technological advancements. Future research will focus on data collection, algorithm selection, and clinical validation to ensure the system's effectiveness and responsible integration into Ayurvedic practice.