Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Accurate prediction of individual health outcomes remains a critical challenge in preventive medicine, with rising demand for personalized healthcare solutions. Feed-Forward Neural Networks (FFNN) is used to refine recommendations over time based on user feedback and evolving health conditions. To enhance user interaction, the system integrates Health-GPT, an AI-driven assistant that offers real-time, conversational health insights and recommendations based on FFNN outputs. This integration of Natural Language Processing (NLP) allows for the analysis of unstructured medical texts and patient interactions, enhancing recommendation precision. Furthermore, federated learning ensures data privacy by enabling decentralized model training without exposing sensitive patient information. This solution aims to improve preventive healthcare by providing tailored guidance, empowering users to make informed wellness decisions. From the results, it is observed that by using FFNN model an accuracy of 99.09% is achieved.
Keywords:
Accurate prediction, Personalized Healthcare, Feed-Forward Neural Network (FFNN), Preventive measures
Cite Article:
"Personalized Health Prediction using Feed-Forward Neural Network", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 3, page no.b541-b547, March-2025, Available :http://www.ijrti.org/papers/IJRTI2503181.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator