Predicting Participation in Cancer Screening Programs with Machine Learning
This work addresses improving participation in South Korea's National Cancer Screening Program, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled predicting participation in cancer screening programs in South Korea using machine learning models, with the best model achieving an AUC-ROC of 0.8706 and average precision of 0.8776.
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.