Hyperparameter tuning results for sarcopenia and MQI.

Hyperparameter tuning results for sarcopenia and MQI.

<p>Hyperparameter tuning results for sarcopenia and MQI.</p>

Saved in:

Bibliographic Details
Main Author:	Naiara Virto (20490408) (author)
Other Authors:	Danielle Marie Dequin (20490411) (author), Xabier Río (20490414) (author), Amaia Méndez-Zorrilla (11611312) (author), Begoña García-Zapirain (4442677) (author)
Published:	2024
Subjects:	Cell Biology Genetics Neuroscience Biotechnology Developmental Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified provide crucial information predict either condition potential future integration individual &# 8217 bilbao &# 8217 artificial intelligence opens 13 &# 177 older adult population predictive models used socioeconomic factors associated examines determining factors xlink "> exploring 1253 older adults 78 voluntarily participated key identifying factors xlink "> sarcopenia xlink "> older adults key factors classification models thereby improving successful algorithms study underscores study highlights single factor significant determinants selected features sectional study relative power overall health new opportunities may slow machine learning holistic approach feature selection feature datasets early detection designing personalized descriptive cross constructed according clinical markers affect sarcopenia adverse effects addressing sarcopenia
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!

Cannot write session to /tmp/vufind_sessions/sess_2rs20q7htacru8nr6jrd12v412