A machine-learning model based on Transformer architecture, a form of artificial intelligence originally developed for language processing, can be used to detect heart disease from electrocardiogram (ECG), according to research in the International Journal of Medical Engineering and Informatics. Tests show it works well with data from several well-known medical datasets.
Heart disease is a major healthcare problem, with almost 18 million dying prematurely each year because of it. The challenge if finding ways to detect cardiovascular disease early enough to make treatment effective. An ECG is the standard way to record the heart’s electrical activity and is thus a common diagnostic tool. Interpretation of the trace needs significant expertise, is time-consuming and is not without the risk of misinterpretation.
The researchers discusses a one-dimensional (1D) Transformer model that can analyse ECG signals and in parallel with other clinical data. In tests, it was up to 94.2 per cent accurate in spotting the early stages of heart disease. Such precision coupled with expert clinical assessment can suffice to give the healthcare team more reliable options in taking a patient on to the next step in diagnosis and potential treatment.
The researchers suggest that their approach needs further development and validation with independent clinical datasets before it can be tested in a live clinical setting.
Miloud Aouidate, A. (2026) ‘Heart disease detection using 1D transformer network: case of ECG signals and clinical data’, Int. J. Medical Engineering and Informatics, Vol. 18, No. 5, pp.1–18.
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