Semantic-Filtered SMOTE-PSO for Breast Cancer Trial Eligibility Classification - Taslim; Mumtazimah Mohamad (Journal of Electronics, Electromedical Engineering, and Medical Informatics, 2026)
This study addresses breast cancer clinical trial eligibility classification from free-text criteria under severe class imbalance, a condition that biases learning toward the majority class and complicates screening decisions when false positives and false negatives carry different operational costs. The study evaluates whether semantic plausibility control and optimization improve classification performance and screening-oriented error trade-offs under imbalanced conditions. The main contribution of this study is the proposed BEACoN framework, which integrates semantic-filtered augmentation and PSO-guided optimization within a unified screening-oriented eligibility classification setting. Four BioBERT-BiLSTM variants were evaluated using fixed train-validation-test partitions across three random seeds: a baseline model (M1), SMOTE augmentation (M2), SMOTE with cosine filtering (M2.5), and the proposed BEACoN framework (M3). Performance was evaluated using Precision, Recall, F1, AUROC, and AUPRC with pooled multi-seed statistical analysis to improve robustness and reduce single-seed bias. The evaluated augmentation-based configurations achieved pooled F1 scores up to 0.9381 ± 0.0005, AUROC up to 0.9976 ± 0.0001, and AUPRC up to 0.9808 ± 0.0004, indicating improved screening-oriented classification performance relative to the baseline. However, SMOTE with cosine filtering behaved broadly similarly to standard SMOTE under the evaluated embedding setting, indicating that the selected cosine threshold functioned largely as a permissive constraint, although modest seed-dependent prediction differences were still observed. Although BEACoN did not demonstrate statistically significant superiority over SMOTE in aggregate performance, it provided a more balanced false-positive and false-negative trade-off under comparable classification performance. Overall, the findings suggest that plausibility-controlled augmentation may provide practical value for screening-oriented eligibility classification under severe class imbalance