AI-Based Health Monitoring Systems Techniques, Applications and Challenges

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@article{1712279,
      author = {Prof. Bharat Tank, Shikha Shah, Shruti Maradiya, Priya Patel, Rinkal Bariya},
      title = {AI-Based Health Monitoring Systems Techniques, Applications and Challenges},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {5},
      pages = {2544-2550},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712279.pdf},
      abstract = {In today?s fast-paced environment, routine health tracking plays a vital role in detecting chronic illnesses at an early stage. This work introduces HealthGuard, an AI-driven platform that leverages user-supplied health information to predict risks of diabetes, cardiovascular disease, and respiratory conditions. The system utilizes a Random Forest algorithm to ensure dependable and precise predictions. To make interaction more natural, it incorporates a chatbot capable of interpreting everyday language and offering immediate, relevant feedback. Additionally, an interactive dashboard records user history and visualizes prediction patterns, helping individuals stay informed and adopt preventive measures. This paper details the system?s design, development, and evaluation, emphasizing how predictive modeling combined with conversational AI can enable users to manage their health more effectively.},
      keywords = {AI Chatbot, Disease Prediction, Health Monitoring, Symptom Analysis, Predictive Health, and User Dashboard},
      month = {November},
      doi = {https://doi.org/10.64388/IREV9I5-1712279}
  }