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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM62789

AI-DRIVEN PUBLIC CHATBOT FOR DIABETES DISEASE AWARENESS

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62789

Volume:

10 |

5 |

May 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

KS

Khushi Singh

BCA

Jspm University

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SS

Shubham Solanke

BCA

Jspm University

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AS

Ankit Sirsat

BCA

Jspm University

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Abstract

Diabetes is one of the most widespread chronic health conditions affecting millions of individuals across the world. Lack of proper awareness regarding symptoms, risk factors, and preventive measures often results in delayed diagnosis and serious health complications. Early detection and awareness can significantly reduce the risk of long-term health problems associated with diabetes. With the rapid growth of artificial intelligence and web technologies, intelligent healthcare applications are being developed to assist individuals in monitoring their health conditions and promoting disease awareness.This research proposes an AI-driven public chatbot designed to increase awareness about diabetes and provide basic guidance related to diabetes risk. The chatbot interacts with users through a web-based interface where individuals can provide simple health information such as glucose level, body mass index (BMI), blood pressure, age, and family history. Based on these inputs, the system evaluates the potential diabetes risk using a rule-based predictive model.The proposed system is implemented using the Flask web framework and integrates a conversational chatbot interface that allows users to interact easily with the application. The chatbot analyzes the provided health parameters and classifies the diabetes risk into three categories: low risk, moderate risk, and high risk. In addition to risk prediction, the system also provides awareness messages and health recommendations to encourage users to adopt healthy lifestyle habits.The experimental implementation demonstrates that AI-based chatbots can be effectively used as simple and accessible tools for public healthcare awareness. The system is designed to be lightweight, user-friendly, and scalable, making it suitable for community-level health awareness programs. By promoting early awareness and preventive healthcare practices, the proposed chatbot system can contribute to reducing the impact of diabetes in society.

KEYWORDS

Keywords: Artificial Intelligence Diabetes Awareness Healthcare Chatbot Machine Learning Preventive Healthcare Web Application.

Cite This Article

Khushi Singh, Shubham Solanke, Ankit Sirsat, "AI-DRIVEN PUBLIC CHATBOT FOR DIABETES DISEASE AWARENESS," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-5, May 2026.

Full Article/Conclusion

This research presented an AI-driven public chatbot designed to increase awareness about diabetes disease and assist individuals in understanding potential risk factors. The system integrates artificial intelligence with web technologies to create an interactive healthcare awareness platform.By allowing users to input basic health parameters and receive immediate feedback, the chatbot encourages individuals to monitor their health conditions and adopt preventive healthcare practices. The conversational interface makes the system easy to use and accessible to a wide range of users.The implementation results demonstrate that AI-based chatbot systems can effectively support public healthcare awareness initiatives. Such systems are cost-effective, scalable, and capable of providing continuous assistance to users.The proposed chatbot does not replace professional medical diagnosis, but it serves as a helpful tool for promoting awareness and encouraging early preventive action.

References

[1] I. Naveed, M. F. Kaleem, K. Keshavjee, and A. Guergachi, “Artificial intelligence with temporal features outperforms machine learning in predicting diabetes,” PLOS Digital Health, vol. 2, no. 10, 2023. [2] A. A. Khan, “A comprehensive review on diabetes disease prediction and risk modeling using machine learning algorithms,” International Journal of Intelligent Systems and Applications in Engineering, 2023. [3] A. J. Agrawal, R. R. Welekar, N. Parati, P. R. Satav, L. H. Patil, and S. S. Aote, “Diabetes prediction using medical data and disease influence measures using machine learning,” International Journal of Intelligent Systems and Applications in Engineering, 2023. [4] P. D. Petridis, A. S. Kristo, A. K. Sikalidis, and I. K. Kitsas, “A review on trending machine learning techniques for type 2 diabetes mellitus management,” Informatics, vol. 11, no. 4, 2024. [5] V. J. Magar, S. B. Bhoite, R. S. Mente, and T. B. Pisal, “AI-powered insights into diabetes mellitus: A comprehensive systematic review,” International Journal of Intelligent Systems and Applications in Engineering, 2024. [6] S. Aouamria, “An ensemble deep learning model for diabetes disease prediction,” International Journal of Intelligent Systems and Applications in Engineering, 2024. [7] Y. Sui, “Diabetes prediction based on machine learning,” Theoretical and Natural Science, 2024. [8] Q. Guo, B. Fu, Y. Tian, S. Xu, and X. Meng, “Recent progress in artificial intelligence and machine learning for novel diabetes mellitus medication development,” Current Medical Research and Opinion, 2024. [9] P. B. Khokhar, C. Gravino, and F. Palomba, “Advances in artificial intelligence for diabetes prediction: Insights from a systematic literature review,” Artificial Intelligence in Medicine, 2025. [10] M. Kiran, Y. Xie, N. Anjum, G. Ball, B. Pierscionek, and D. Russell, “Machine learning and artificial intelligence in type 2 diabetes prediction: A comprehensive bibliometric and literature analysis,” Frontiers in Digital Health, 2025.

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Publication Date

5/9/2026

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Double-Blind Peer Review

Review Rounds: 2

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