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DTSTAMP:20260809T191459Z
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DTSTART;TZID=America/New_York:20260808T100000
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DESCRIPTION:Abstract: Cardiovascular disease (CVD) is one of the leading ca
 uses of death worldwide\, making early and accurate prediction essential f
 or improving patient outcomes. Traditional machine learning approaches req
 uire centralized medical data\, which raises concerns about patient privac
 y and security. This project proposes a privacy-preserving cardiovascular 
 disease prediction system using a Federated Learning (FL) model\, where mu
 ltiple healthcare institutions collaboratively train a shared model withou
 t exchanging sensitive patient data. Each institution trains the model loc
 ally\, and only encrypted model updates are shared with a central server f
 or aggregation. This approach protects patient confidentiality while achie
 ving prediction performance comparable to centralized models. The proposed
  framework enhances privacy\, security\, scalability\, and collaboration a
 mong healthcare organizations\, making it a reliable solution for intellig
 ent and secure cardiovascular disease prediction.\n\nSpeaker(s): Shirisha\
 , \n\nAgenda: \n10.00 am - Introduction about the speaker\n\n10.05 am - Ta
 lk\n\n10.45 am - Q&A\n\n11.00 am - Conclusion\n\nVirtual: https://events.v
 tools.ieee.org/m/570198
LOCATION:Virtual: https://events.vtools.ieee.org/m/570198
ORGANIZER:mguduri@ltu.edu
SEQUENCE:23
SUMMARY:Privacy Preservation Technique in Cardiovascular Disease Prediction
  Using Federated Learning Model
URL;VALUE=URI:https://events.vtools.ieee.org/m/570198
X-ALT-DESC:Description: <br /><p>Abstract: Cardiovascular disease (CVD) is 
 one of the leading causes of death worldwide\, making early and accurate p
 rediction essential for improving patient outcomes. Traditional machine le
 arning approaches require centralized medical data\, which raises concerns
  about patient privacy and security. This project proposes a privacy-prese
 rving cardiovascular disease prediction system using a Federated Learning 
 (FL) model\, where multiple healthcare institutions collaboratively train 
 a shared model without exchanging sensitive patient data. Each institution
  trains the model locally\, and only encrypted model updates are shared wi
 th a central server for aggregation. This approach protects patient confid
 entiality while achieving prediction performance comparable to centralized
  models. The proposed framework enhances privacy\, security\, scalability\
 , and collaboration among healthcare organizations\, making it a reliable 
 solution for intelligent and secure cardiovascular disease prediction.</p>
 <br /><br />Agenda: <br /><p>10.00 am - Introduction about the speaker</p>
 \n<p>10.05 am - Talk&nbsp\;</p>\n<p>10.45 am - Q&amp\;A</p>\n<p>11.00 am -
  Conclusion</p>
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