AI-Powered Personalized Preventive Healthcare System Using Predictive Risk Modeling and Lifestyle Analytics
DOI:
https://doi.org/10.5281/ijurd.v2i3.26Keywords:
Natural Language Processing, Clinical Text, BioBERT, Named Entity Recognition, Medical Reports, Text MiningAbstract
Preventive healthcare is becoming increasingly important for reducing the burden of chronic diseases and improving overall population health. This paper presents an AI-Powered Personalized Preventive Healthcare System that leverages predictive risk modeling and lifestyle analytics to enable early intervention and disease prevention. The proposed framework integrates data from wearable devices, electronic health records, and patient lifestyle inputs such as diet, physical activity, and sleep patterns. Machine learning and deep learning models are employed to analyze risk factors and predict the likelihood of developing conditions such as cardiovascular disease, diabetes, and hypertension. The system provides personalized recommendations, including lifestyle modifications and preventive measures, tailored to individual health profiles. Adaptive learning mechanisms are incorporated to continuously refine predictions based on new data. Additionally, explainability modules are included to ensure transparency and user trust. Experimental results demonstrate improved prediction accuracy and user engagement compared to traditional preventive approaches. Integration with prior research in healthcare analytics enhances system robustness and scalability. The study highlights the potential of AI-driven preventive systems in transforming healthcare from reactive treatment to proactive health management.
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Copyright (c) 2026 Tanvi Jain, Ramesh Kumar
This work is licensed under a Creative Commons Attribution 4.0 International License.
