Precision-Recall curves comparing 7 machine learning models and a baseline value.

Precision-Recall curves comparing 7 machine learning models and a baseline value.

<p>PR curves are computed using the withheld test data. SVM is the model with the highest area under the curve.</p>

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Bibliographic Details
Main Author: Andres Portocarrero-Bonifaz (21367929) (author)
Other Authors: Salman Syed (18880470) (author), Maxwell Kassel (21367932) (author), Grant W. McKenzie (21367935) (author), Vishwa M. Shah (21367938) (author), Bryce M. Forry (21367941) (author), Jeremy T. Gaskins (9281129) (author), Keith T. Sowards (21367944) (author), Thulasi Babitha Avula (21367947) (author), Adrianna Masters (21367950) (author), Jose G. Schneider (21367953) (author), Scott R. Silva (14975143) (author)
Published: 2025
Subjects:
Biotechnology
Cancer
Infectious Diseases
Virology
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
without grade 3
support vector machines
rectum differed significantly
predicting grade 3
predict grade 3
normmcc testing scores
gaussian naive bayes
f1 testing scores
external beam radiotherapy
charlson comorbidity index
applying exclusion criteria
achieving satisfactory performance
accuracy testing scores
deep learning networks
creating multivariable models
final model performance
machine learning presents
predict patient outcomes
advancing patient care
machine learning models
traditional statistical analysis
predict specific toxicities
high dose rate
gynecological cancer patients
underwent hdr brachytherapy
machine learning
applied models
ranking model
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women worldwide
toxicity data
retrospective analysis
recent studies
random forest
prevalent cancers
ovoid applicators
often used
novel solution
nearest neighbors
logistic regression
hyperparameter tuning
higher toxicities
dosimetric variables
data availability
characterized using
233 patients
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