BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/New_York
X-LIC-LOCATION:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20250626T233527Z
LOCATION:B302-B305
DTSTART;TZID=America/New_York:20241120T100000
DTEND;TZID=America/New_York:20241120T170000
UID:submissions.supercomputing.org_SC24_sess533_post212@linklings.com
SUMMARY:Machine Learning Applications for Early-Stage Ovarian Cancer Diagn
 osis
DESCRIPTION:Delina Mekonnen and Kazem Cheshmi (McMaster University, Ontari
 o, Canada)\n\nOvarian cancer (OC) significantly impacts women's health, an
 d despite its prevalence, remains without a definitive cure. Early detecti
 on is crucial for improving treatment outcomes and reducing mortality rate
 s and healthcare system costs. Leveraging advancements in machine learning
 , our study seeks to empower physicians with tools for more confident and 
 timely diagnosis. This study introduces a novel approach using machine lea
 rning to enhance early-stage OC diagnosis. We propose the Data Driven Diag
 nosis Framework (DDD), a new feature extraction and ensemble method that i
 mproves classification accuracy. Using models such as Random Forest, Logis
 tic Regression, Decision Tree, Gradient Boosting Machine, Extreme Gradient
  Boosting Machine, and language models, our approach shows accuracy improv
 ements of up to 14%-28% over state-of-the-art methods.\n\nRegistration Cat
 egory: Tech Program Reg Pass, Exhibits Reg Pass\n\nSession Chairs: Ayesha 
 Afzal (Friedrich-Alexander University, Erlangen-Nuremberg; Erlangen Nation
 al High Performance Computing Center); Sally Ellingson (University of Kent
 ucky); and Alan Sussman (University of Maryland)\n\n
END:VEVENT
END:VCALENDAR