# AMS-1D-CNN

Early ovarian cancer diagnosis leans on serum markers and routine laboratory tests, yet redundant, noisy and correlated variables in high-dimensional clinical data degrade both classification and interpretability. A hybrid biomarker selection pipeline fuses correlation analysis, mutual information, XGBoost importance and SHAP relevance with particle-swarm-optimized adaptive weights, feeding an attention-enhanced multi-scale 1D convolutional network; on a public Kaggle ovarian cancer clinical dataset under stratified 10-fold cross-validation it reports 93.35% accuracy and 90.66% ROC-AUC. In ablation, the full pipeline reaches 85.51% accuracy, 19.33 percentage points above a baseline 1D-CNN without selection or attention.

Early ovarian cancer diagnosis leans on serum markers and routine laboratory tests, yet redundant, noisy and correlated variables in high-dimensional clinical data degrade both classification and interpretability. A hybrid biomarker selection pipeline fuses correlation analysis, mutual information, XGBoost importance and SHAP relevance with particle-swarm-optimized adaptive weights, feeding an attention-enhanced multi-scale 1D convolutional network; on a public Kaggle ovarian cancer clinical dataset under stratified 10-fold cross-validation it reports 93.35% accuracy and 90.66% ROC-AUC. In ablation, the full pipeline reaches 85.51% accuracy, 19.33 percentage points above a baseline 1D-CNN without selection or attention.