A Robustness Evaluation of Machine Learning Algorithms for ECG Myocardial Infarction Detection.

ABSTRACT: An automatic electrocardiogram (ECG) myocardial infarction detection system needs to satisfy several requirements to be efficient in real-world practice. These requirements, such as reliability, less complexity, and high performance in decision-making, remain very important in a realistic clinical environment. In this study, we investigated an automatic ECG myocardial infarction detection system and presented a new approach to evaluate its robustness and durability performance in classifying the myocardial infarction (with no feature extraction) under different noise types. We employed three well-known supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF), and tested the performance and robustness of these techniques in classify

SUBMITTER: Sraitih M 

PROVIDER: S-EPMC9456488 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature