Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review.

ABSTRACT: Heart disease is one of the leading causes of mortality throughout the world. Among the different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non-invasive procedure. However, the following are challenges: the scarcity of medical experts, the complexity of ECG interpretations, the manifestation similarities of heart disease in ECG signals, and heart disease comorbidity. Machine learning algorithms are viable alternatives to the traditional diagnoses of heart disease from ECG signals. However, the black box nature of complex machine learning algorithms and the difficulty in explaining a model's outcomes are obstacles for medical practitioners in having confidence in machine learning models. This observation paves the way for interpretable machine learning (I

SUBMITTER: Ayano YM 

PROVIDER: S-EPMC9818170 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature