Web Application for Recommendation of Ayurvedic Drugs and Medicine using ML

Pradnesh Amar  Shevantikar; Avinash Prabhakar  Udata; Abhishek Siddharam  Korachagao

doi:10.48001/jodpba.2024.1213-18

Authors

Pradnesh Amar Shevantikar Department of Computer Science and Engineering, SVERI’s College of Engineering, Pandharpur, Maharashtra,
Avinash Prabhakar Udata Department of Computer Science and Engineering, SVERI’s College of Engineering, Pandharpur, Maharashtra, India
Abhishek Siddharam Korachagao Department of Computer Science and Engineering, SVERI’s College of Engineering, Pandharpur, Maharashtra,

DOI:

https://doi.org/10.48001/jodpba.2024.1213-18

Keywords:

Ayurvedic, Database, Drugs, Machine learning, Web application

Abstract

This project aims to develop a comprehensive Ayurvedic drug recommendation website that seamlessly integrates traditional Ayurvedic principles with modern technology. The platform will cater to both students seeking educational resources and practitioners offering personalized healthcare guidance. The website's key features include drug prediction algorithms, herbal drug recommendations based on individualized assessments on symptoms, and a user-friendly interface. The initial phase involves meticulous planning, scoping, and research to define project objectives, target audiences, and select an appropriate technology stack. Subsequent weeks focus on design and prototyping, incorporating user feedback to ensure an intuitive and engaging interface. The front-end development phase emphasizes responsiveness and usability, while the back-end development integrates sophisticated algorithms for drug prediction and drug recommendation. User authentication and authorization mechanisms will be implemented to cater to both students and practitioners, ensuring a secure and personalized experience. The website's robustness and reliability will be validated through comprehensive testing, addressing any identified issues to guarantee a seamless user experience. The refinement and optimization phase involve continuous feedback collection, iterative improvements, and performance optimization. Beta testing will be conducted to gather user insights, refining the platform further. The deployment and launch process will involve hosting the website on a reliable platform, setting up monitoring tools, and officially releasing the Ayurvedic drug prediction and drug recommendation website to the public. This project combines the rich heritage of Ayurveda with cutting-edge technology, offering a valuable resource for education and healthcare in the Ayurvedic domain. The website aims to empower both students and practitioners with accurate drug predictions and personalized drug recommendations based on symptoms, fostering a holistic approach to healthcare that aligns with traditional Ayurvedic principle.

Downloads

Download data is not yet available.

References

Aakash, B., & Srilakshmi, A. (2021). MAGE: An Efficient Deployment of Python Flask Web Application to App Engine Flexible Using Google Cloud Platform. In Inventive Communication and Computational Technologies: Proceedings of ICICCT 2020 (pp. 59-68). Springer Singapore. https://doi.org/10.1007/978-981-15-7345-3_5.

Looney, S. (2023). Content moderation through removal of service: Content delivery networks and extremist websites. Policy & Internet, 15(4), 544-558. https://doi.org/10.1002/poi3.370.

Muthappan, S., Elumalai, R., Shanmugasundaram, N., Johnraja, N., Prasath, H., Ambigadoss, P., ... & Ponnaiah, M. (2022). AYUSH digital initiatives: Harnessing the power of digital technology for India’s traditional medical systems. Journal of Ayurveda and Integrative Medicine, 13(2), 100498. https://doi.org/10.1016/j.jaim.2021.07.014.

Schwarzl, M., Borrello, P., Kogler, A., Varda, K., Schuster, T., Schwarz, M., & Gruss, D. (2022, September). Robust and scalable process isolation against spectre in the cloud. In European Symposium on Research in Computer Security (pp. 167-186). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-17146-8_9.

Shah, D. R., & Dhawan, D. A. (2022). Disease Prediction Based on Symptoms Using Various Machine Learning Techniques. In Computational Intelligence and Data Analytics: Proceedings of ICCIDA 2022 (pp. 141-152). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-19-3391-2_10.

Silva, M. A., Franco, M. F., Scheid, E. J., Zembruzki, L., & Granville, L. Z. (2024, April). PerfResolv: A Geo-Distributed Approach for Performance Analysis of Public DNS Resolvers Based on Domain Popularity. In International Conference on Advanced Information Networking and Applications (pp. 35-47). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-57853-3_4.

Vohra, R., Singh, R., & Shrivastava, R. (2024). A scoping review on ‘Maharishi Amrit Kalash’, an ayurveda formulation for cancer prevention and management. Journal of Ayurveda and Integrative Medicine, 15(1), 100866. https://doi.org/10.1016/j.jaim.2023.100866.