AI-Driven Lifestyle Chatbot for Personalized Disease Risk Prediction and Preventive Health Guidance

References

[1] N. Abdulhadi, A. Al-Mousa, "Diabetes detection using machine learning classification methods," in 2021 International Conference on Information Technology (ICIT), pp. 350 354. IEEE, 2021.

[2] S. M. Ganie, B. B. Reddy, K. Hemachandran, M. Rege, "An investigation of ensemble learning techniques for obesity risk prediction using lifestyle data," Decision Analytics J, vol. 100539, 2024.

[3] A. K. Mishra, P. K. Keserwani, S. G. Samaddar, H. B. Lamichaney, A. K. Mishra, "A decision support system in healthcare prediction," in Advanced Computational and Communication Paradigms, Singapore, pp. 156-167, 2018.

[4] M. Patil, V. B. Lobo, P. Puranik, A. Pawaskar, A. Pai, R. Mishra, "A proposed model for lifestyle disease prediction using support vector machine," in 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1-6. IEEE, 2018.

[5] L. Ren, T. Wang, H. Zhang, A. Bouras, "A prediction framework for lifestyle-related disease prediction using healthcare data," in 2023 International Conference on Computer Applications Technology (CCAT), pp. 94-99. IEEE, 2023.

[6] M. Rezaee, I. Putrenko, K. Grolinger, M. A. M. Capretz, "Development and validation of prediction models for multiple cardiovascular diseases and type 2 diabetes," PLoS ONE, vol. 15, no. 7, p. e0235758, 2020.

[7] C. Sai Ganesh, R. Sai Kishore, B. K. Reddy, S. K. Rout, "Data-driven disease prediction and lifestyle monitoring system," in 2024 1st International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU). IEEE, 2024.

[8] V. Shorewala, "Early detection of coronary heart disease using ensemble techniques," Informatics Med Unlocked, vol. 26, p. 100655, 2021.

[9] A. Tiwari, A. Chugh, A. Sharma, "Ensemble framework for cardiovascular disease prediction," Comput Biol Med, vol. 146, p. 105624, 2022.

[10] S. Uddin, A. Khan, M. E. Hossain, M. A. Moni, "Comparing different supervised prediction," BMC Med Inform Decis Mak, vol. 19, no. 1, p. 16, 2019.

[11] W. Yu, T. Liu, R. Valdez, M. Gwinn, M. J. Khoury, "Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre diabetes," BMC Med Inform Decis Mak, vol. 10, p. 16, 2010.

[12] S. D. Gharge and P. S. Patil, “Chat-bot health-care application using artificial intelligence,” in 2024 International Conference on Artificial Intelligence and Data Engineering (AIDE), pp. 1–5, 2024.

[13] A. Babu and S. B. Boddu, “BERT-Based Medical Chatbot: Enhancing Healthcare Communication through Natural Language Understanding,” Exploratory Research in Clinical and Social Pharmacy, vol. 13, pp. 1–9, 2024.

[14] J. Hayat, M. Lari, M. AlHerz, and A. Lari, “The Utility and Limitations of Artificial Intelligence-Powered Chatbots in Healthcare,” Cureus, vol. 16, no. 11, pp. 1–8, 2024.

[15] M. H. Kurniawan, H. Handiyani, and T. Nuraini, “A Systematic Review of Artificial Intelligence-Powered Chatbot Intervention for Managing Chronic Illness,” Annals of Medicine, vol. 56, no. 1, pp. 1–12, 2024.

[16] A. Wah, “Revolutionizing E-Health: The Transformative Role of AI-Powered Hybrid Chatbots in Healthcare Solutions,” Frontiers in Public Health, vol. 13, pp. 1–10, 2025.

[17] S. Sirpurwar, V. Umare, B. Umare, R. Rathod, and C. Jogekar, “AI-Based Chatbot for Healthcare,” International Journal of Engineering Research & Technology (IJERT), vol. 14, no. 12, pp. 1–6, 2025.

[18] S. Khan and M. Gupta, “Network-Integrated Medical Chatbot for Enhanced Healthcare Services,” Telematics and Informatics Reports, vol. 8, pp. 1–7, 2024.

[19] R. Perez, J. Gomez, and L. Martinez, “Design and Safety Evaluation of an AI Chatbot for Health Promotion in Chronic Diseases,” Procedia Computer Science, vol. 248, pp. 52–59, 2024.2021.

[20] N. Yan and G. Alterovitz, “A General-Purpose AI Avatar in Healthcare,” arXiv preprint arXiv:2401.12981, pp. 1–12, 2024

@article{1715177,
      author = {S. Geeviga, Karshavarthini M, Tamilarasan S, Shahithya R, Mouli B},
      title = {AI-Driven Lifestyle Chatbot for Personalized Disease Risk Prediction and Preventive Health Guidance},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {9},
      pages = {3549-3554},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715177.pdf},
      abstract = {Health Predict Chatbot is an AI-driven web application designed to predict lifestyle-related disease risks and provide personalised preventive health guidance. The system collects user lifestyle information such as screen time, break frequency, posture, ergonomics, and BMI through an interactive interface. Using machine learning algorithms, it analyses the data and predicts potential conditions including digital eye strain, carpal tunnel syndrome, tech neck, and burnout. The application presents results through a visual dashboard and generates diet recommendations and preventive suggestions. Developed using Python, Streamlit and scikit-learn, the system promotes early awareness and preventive healthcare while operating in offline mode.},
      keywords = {Artificial Intelligence, Lifestyle Diseases, Health Prediction, Machine Learning, Preventive Healthcare.},
      month = {March},
      doi = {https://doi.org/10.64388/IREV9I9-1715177}
  }