Predicting Participation in Cancer Screening Programs with Machine Learning. (arXiv:2101.11614v1 [q-bio.OT])

In this paper, we present machine learning models based on random forest
classifiers, support vector machines, gradient boosted decision trees, and
artificial neural networks to predict participation in cancer screening
programs in South Korea. The top performing model was based on gradient boosted
decision trees and achieved an area under the receiver operating characteristic
curve (AUC-ROC) of 0.8706 and average precision of 0.8776. The results of this
study are encouraging and suggest that with further research, these models can
be directly applied to Korea’s healthcare system, thus increasing participation
in Korea’s National Cancer Screening Program.



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