AyurWeb: A Coalesce of Ayurveda and Modern Technology

Abstract

\"AyurWeb: A Coalesce of Ayurveda and Modern Technology,\" a web-based platform that employs modern computing resources to analyze and provide personalized Ayurvedic information. AyurWeb\'s primary purpose is to analyze and predict an individual\'s prakriti — or body constitution — according to the practice of classical Ayurveda while utilizing machine learning and web technology as backbone. It is a frontend created with HTML, CSS, JavaScript, and Bootstrap with a backend developed in PHP and SQL with the XAMPP server stack. The site uses a login and registration system to ensure authenticated user access to the core functionality of the site. The main feature is an interactive quiz that has ten questions and relies on a Python Flask API that processes the responses through a Naive Bayes Classification model. The model is trained from labelled prakriti datasets and produces-outcome of predicted prakriti as one of seven types: three doshic (vata, pitta, kapha), three duals (vata-pitta, pitta-kapha, vata-kapha), or tridoshic; and provides users with personalized recommendations about their daily routines, eating, or health prevention based on their predicted prakriti. In addition to predicting prakriti, the site has a blog module that allows users to contribute; users can link social media accounts, and display educational information about doshas and to raise awareness and reduce misinformation about Ayurveda. This shows that AyurWeb is an example of a scalable digital health framework.

Introduction

AyurWeb is a web-based prototype that blends traditional Ayurvedic principles with modern machine learning to classify users' prakriti (dosha types) and offer personalized lifestyle recommendations. It uses a Naive Bayes classifier hosted on a Python Flask server, with a user-friendly front end built using HTML, CSS, JavaScript, and Bootstrap. User data is managed securely via a PHP-SQL backend running on XAMPP.

The system processes quiz responses to predict dosha types and provides detailed lifestyle guidance, aiming to enhance user engagement and credibility of Ayurveda through accessible digital tools. Though currently functional, AyurWeb has room for improvement in areas such as data security, diagnostic precision, scalability (e.g., MongoDB), global deployment, and model interpretability.

In summary, AyurWeb represents a promising convergence of ancient wellness systems and modern AI, laying the foundation for future developments in personalized digital health rooted in traditional knowledge.

Conclusion

AyurWeb represents a ground-breaking marriage of classical Ayurvedic philosophy and contemporary web-based technologies, providing a functional prototype in the area of prakriti (dosha) classification and personalized lifestyle recommendations. AyurWeb combines a secure registration/login process for users, a filename structured user database, and a Naive Bayes model that has been trained and brought live on a Python Flask server; all of which can help to classify dosha prediction based on quiz answers. The front-end was built in HTML, CSS, JavaScript and Bootstrap, and provides users with accessible input and navigable output. AyurWeb indicates an interesting complimentary application of machine learning to traditional medicine\'s data models, especially from the perspective of user involvement, with a proper spectral data pipeline with organized data, and an interactive web platform. The backend server produced from PHP and SQL, with XAMPP for local host and easy manipulation of user and storage information of users and their interactions, is a significant element to user security and experience. Finally, AyurWeb contributes to increasing the public awareness and credibility of Ayurveda by providing extensive lifestyle reports that address the user\'s context, all of which supports building user trust and functioning knowledge. While the current implementation presents a functional system with potential educational value, there is considerable opportunity to develop the scope of the system on a variety of dimensions, including better cyber-hygiene, developing the diagnostic resolution of the quiz, switching to more scalable technologies like MongoDB, deploying for global access, and exploring more advanced, interpretable models for prakriti prediction. In essence, AyurWeb serves as a meaningful step toward bridging ancient health sciences and emerging computing methodologies, opening up further avenues for innovation in personalized wellness technologies.

References

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Copyright

Copyright © 2025 Nirmit Hatti, Yuganti Hatwar, Tanaya Hartalekar, Harshvardhan Rawat, Om Hiwarkar, Makarand Upkare. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.