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AI-Driven Lifestyle Chatbot for Personalized Disease Risk Prediction and Preventive Health Guidance
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence, Machine Learning
DOI: https://doi.org/10.64388/IREV9I9-1715177
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.
How to cite this paper
@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}
}