Electrocardiograms (ECG) provide a way to identify potential cases of Chagas disease, a parasitic disease in Central and South America. Despite the usefulness of 12-lead ECG records for the disease screening, the patient records are insufficient and many of them are self-reported, containing large number of false labels. To achieve a generalisable and accurate ECG analysis model, we propose a semi-supervised noise label learning approach to tackle the challenges by leveraging ECG records from several datasets, including SaMi-Trop dataset - a small dataset with reliable labels, PTB-XL dataset with large amount of healthy group data and CODE-15% dataset with self-reported disease cases. After training a base model by using samples from SaMi-Trop dataset, JS divergence is applied to align data distributions of samples from these heterogenous data sources. Following it, we further use data from healthy group and pseudo-labelled data filtered by confidence level from self-reported group to fine-tune our model. We achieve 0.9064 in terms of the TPR @ 5 % score by using the validation set from the CODE-15% dataset.