Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.

Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.

<p>Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.</p>

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Opis bibliograficzny
1. autor: Raiki Yoshimura (22676573) (author)
Kolejni autorzy: Naotoshi Nakamura (12083048) (author), Takeru Matsuura (22676576) (author), Takeo Toshima (13988776) (author), Takasuke Fukuhara (673475) (author), Kazuyuki Aihara (44090) (author), Katsuhito Fujiu (559488) (author), Shingo Iwami (266092) (author), Tomoharu Yoshizumi (2621434) (author)
Wydane: 2025
Hasła przedmiotowe:
Medicine
urgent need exists
stage liver disease
shorter waiting times
recent years thanks
populations using data
model enabled us
highly heterogeneous sample
appropriate patient care
30 days postoperatively
distinct population similar
different survival times
hierarchical prediction method
better graft quality
survival time
loss population
graft loss
better performance
unexpected infections
transplanted organ
three groups
several models
next categorized
mediated rejection
loss groups
loss group
living donors
gained importance
five groups
conventional models
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Streszczenie:<p>Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.</p>

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