We present our deep learning solution for the 2025 George B. Moody PhysioNet Challenge, which utilizes a teacher-student model architecture. A key component of our approach is a generalized multi-objective teacher model based on the U-Net architecture, which was pre-trained on publicly available 12-lead ECG databases (containing over 1 million ECG recordings) for both the segmentation task and the classification task of 28 cardiac pathologies. The student models were specifically trained to distill knowledge from the teacher's classification outputs, as well as to predict Chagas disease based on public challenge datasets containing Chagas disease labels. To improve the reliability of our predictions, we employed an ensemble of five student models, averaging their outputs during the inference stage. Our model achieved XYth place in the challenge, with a challenge score of 0.458 on the hidden validation set.