Models’ performance when using oversampling to balance the training dataset.

Models’ performance when using oversampling to balance the training dataset.

<p>Models’ performance when using oversampling to balance the training dataset.</p>

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Bibliographic Details
Main Author: Anna Beatriz Silva (20989245) (author)
Other Authors: Elisson da Silva Rocha (17222272) (author), João Fausto Lorenzato (20989248) (author), Patricia Takako Endo (10373252) (author)
Published: 2025
Subjects:
Science Policy
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
three hybridsampling configurations
risk pregnancies earlier
including decision tree
hybridsampling approach resulted
decision tree model
machine learning models
proper data balancing
premature birth predictions
predictive models
biased predictions
obstetric data
data imbalance
timely interventions
sus ).
results show
random forest
preterm births
preterm birth
performance compared
particularly significant
neonatal deaths
neonatal care
minority class
main cause
factors 2
common problem
70 %,
64 %,
37 weeks
000 cases
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