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PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
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TZID:Asia/Kolkata
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DTSTART:19451014T230000
TZOFFSETFROM:+0630
TZOFFSETTO:+0530
TZNAME:IST
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BEGIN:VEVENT
DTSTAMP:20260424T172708Z
UID:2AE08A15-5BD3-4955-B78E-F86EDEF06B91
DTSTART;TZID=Asia/Kolkata:20260424T190000
DTEND;TZID=Asia/Kolkata:20260424T200000
DESCRIPTION:The integration of intelligent healthcare analytics with evolut
 ionary optimization techniques offers a transformative approach to medical
  decision-making and patient care. Healthcare systems generate vast amount
 s of heterogeneous data\, including clinical records\, diagnostic images\,
  and real-time sensor outputs\, which demand advanced computational strate
 gies for meaningful interpretation. Evolutionary algorithms\, inspired by 
 natural selection\, provide robust mechanisms for feature selection\, pred
 ictive modeling\, and optimization of complex healthcare processes. By lev
 eraging these techniques\, intelligent analytics can enhance disease predi
 ction accuracy\, optimize treatment planning\, and support resource alloca
 tion in dynamic clinical environments. Furthermore\, evolutionary optimiza
 tion enables adaptive learning\, ensuring models remain effective across d
 iverse patient populations and evolving medical datasets. This synergy not
  only improves diagnostic precision but also fosters personalized medicine
 \, reducing risks and improving patient outcomes. The proposed framework u
 nderscores the potential of evolutionary computation as a cornerstone in n
 ext-generation healthcare analytics\, bridging data-driven insights with p
 ractical clinical applications.\n\nSpeaker(s): Dr. Jayashree Piri\, \n\nVi
 rtual: https://events.vtools.ieee.org/m/554608
LOCATION:Virtual: https://events.vtools.ieee.org/m/554608
ORGANIZER:amiya87@gmail.com
SEQUENCE:40
SUMMARY:Intelligent Healthcare Analytics using Evolutionary Optimization Te
 chniques
URL;VALUE=URI:https://events.vtools.ieee.org/m/554608
X-ALT-DESC:Description: <br /><p>&nbsp\;&nbsp\;<br>The integration of intel
 ligent healthcare analytics with evolutionary optimization techniques offe
 rs a transformative approach to medical decision-making and patient care. 
 Healthcare systems generate vast amounts of heterogeneous data\, including
  clinical records\, diagnostic images\, and real-time sensor outputs\, whi
 ch demand advanced computational strategies for meaningful interpretation.
  Evolutionary algorithms\, inspired by natural selection\, provide robust 
 mechanisms for feature selection\, predictive modeling\, and optimization 
 of complex healthcare processes. By leveraging these techniques\, intellig
 ent analytics can enhance disease prediction accuracy\, optimize treatment
  planning\, and support resource allocation in dynamic clinical environmen
 ts. Furthermore\, evolutionary optimization enables adaptive learning\, en
 suring models remain effective across diverse patient populations and evol
 ving medical datasets. This synergy not only improves diagnostic precision
  but also fosters personalized medicine\, reducing risks and improving pat
 ient outcomes. The proposed framework underscores the potential of evoluti
 onary computation as a cornerstone in next-generation healthcare analytics
 \, bridging data-driven insights with practical clinical applications.</p>
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