Predicting Participant Attrition in Paediatric Clinical Trials Using Machine Learning and Deep Learning Models

Abstract Participant attrition in paediatric clinical trials remains a major challenge, leading to higher costs, longer trial durations, and compromised research outcomes. Existing retention and prediction methods have shown limited success. This study aimed to predict participant attrition in paediatric clinical trials using machine learning and deep learning models. Secondary data from a paediatric clinical trial in Malawi were used. Logistic regression, Random forest, Multi-layer perceptron, and One convolutional neural network models were trained and evaluated under four data settings: original, SMOTE- enhanced, GAN-augmented, and combined. Predictive performance was assessed using macro-averaged F1-score as the primary metric. Random forest achieved the highest F1- score of 0.514 on the SMOTE-enhanced dataset. Overall performance remained modest, and GAN augmentation did not consistently improve results. The findings suggest that data augmentation techniques provide limited benefits for attrition prediction in this context.