Deep Feature Extraction with Principal Component Analysis for Congenital Heart Disease Classification from Chest Radiographs

This study proposes a reproducible hybrid framework for four-class congenital heart disease classification from chest X-rays by combining ImageNet-pretrained ResNet50 and EfficientNetB3 feature extraction, PCA-based dimensionality reduction, and six conventional machine-learning classifiers. The study provides a controlled comparison of deep feature representations and heterogeneous classifiers under a consistent multiclass protocol. EfficientNetB3–PCA–SVM achieved the best performance with 93.82% accuracy, 0.938 macro-F1, 0.979 specificity, and 0.9947 macro ROC-AUC using only 91 principal components, demonstrating that compact PCA-reduced deep features can provide accurate CHD classification while reducing feature dimensionality and computational complexity.