Quantitative Analysis of Machine and Deep Learning Models in Dysrhythmia Classification

This study presents the quantitative analysis of machine learning and deep learning models in dysrhythmia classification. Existing dysrhythmia classification systems suffer from limited accuracy and adoption by healthcare professionals. This issue encouraged many researchers to adopt machine learning and deep learning models, but these systems don’t demonstrate clear dominance of machine learning and deep learning models. Hence the proposed modified convolution neural network model and comparative study provides a valuable compass for researchers, practitioners, and healthcare professionals, supplying a consolidated understanding of the state-of-the-art and offering a roadmap for further advancements in the pursuit of precise and efficient dysrhythmia classification.