Early detection of Chagas disease is critical for en- abling timely medical intervention and preventing cardio- vascular complications ahead. Electrocardiogram (ECG) signals offer crucial insights into the disease's progres- sion, underscoring the need for advanced signal process- ing and machine learning-based diagnostic tools. This study proposes a novel framework for accurate and effi- cient identification of Chagas disease using standard 12- lead ECG signals. The methodology begins with signal preprocessing to ensure uniformity and reduce noise. Four categories of features are extracted: (a) statistical fea- tures derived from heart rate variability, (b) statistical fea- tures from all 12 ECG leads, and (c) wavelet scattering transform (WST) features computed from leads II, V1, and V3 and (d) patient metadata. These features are input to a RUSBoost classifier, designed to address class im- balance while performing binary classification. The pro- posed framework was evaluated on the PhysioNet/CinC Challenge 2025 training dataset and achieved a Challenge score of 0.266 on the hidden test set under the team name Medics.