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Machine learning and deep learning in healthcare: advancing cardiac arrhythmia classification in healthcare analytics
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| 1.03 MB | Adobe PDF |
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Orientador(es)
Resumo(s)
Cardiac arrhythmias, a global leading disease cause, necessitate rapid, efficient diagnosis.
Shifting from traditional manual electrocardiogram analysis to machine learning approaches
offers enhanced efficiency and accuracy in detection. However, literature research has shown
that long training times and a lack of practical suitability have made implementation difficult
to date. Three prototypes were developed and tested; the results were then used to optimize the
most promising model further. The optimized CNN achieved an overall classification accuracy
of 98.53%. The results are tested for their applicability in a practical context, evaluated, and
compared against existing approaches, resulting in above-average classification outcomes.
Descrição
Palavras-chave
Predictive modeling Convolutional neural networks Deep learning Arrhythmia Classification Hybrid models