Ayurveda herbs recommendations and decease recognition

Abstract

To meet the growing need for natural and reliable healthcare options, the merging of Ayurvedic Herbs with techno has been initiated to deliver personalized health that is easier to access. This enterprise discloses a Full- Stack Symptom Driven Ayurvedic Herbs and Lifestyle Recommendation System empowered by machine learning (ML). The system interprets the symptoms given by the user along with the personal health data such as age, gender, allergies, and medical history to ascertain probable health conditions by means of a Support Vector Classification (SVC) model trained on selected Ayurvedic data sets. It then suggests Ayurvedic formulations, herbal remedies, and lifestyle (diet, yoga, exercise, and daily routines) practices that are preventative and aligned with the predicted condition and Ayurvedic principles. The system comes with a rules- based validation engine that checks for contraindications, age-specific precautions, and ingredient sensitivities, which ensures safety and relevance. The frontend is designed as a modern, user-friendly web interface, whereas the backend constructed with Python frameworks like Flask or FastAPI carries out data preprocessing, disease prediction, and Ayurvedic recommendation, logic apps, and self-care support, especially in the regions where Ayurvedic Herbs is largely trusted and practiced.