Abstract
Cardiac arrhythmias are abnormalities in the regular heartbeat that can significantly negatively impact one’s health. The early diagnosis and precise categorization of these illnesses can be greatly aided by Artificial Intelligence (AI). This work highlights the use of AI algorithms for identifying and categorizing distinct cardiac arrhythmia types utilizing various techniques, including deep learning, Naïve Bayes algorithms, and ECG signal analysis. A Naïve Bayes Algorithm for managing cardiac arrhythmias can be a helpful tool for healthcare professionals in diagnosing and treating the condition. When their effectiveness is compared and assessed using a variety of metrics, such as sensitivity, specificity, accuracy, and F1 score, these AI techniques receive impressive results. The findings demonstrate that AI algorithms can accurately identify and categorize different cardiac arrhythmia types, offering a viable tool for early diagnosis and better patient outcomes. The proposed decision tree-based Cardiac arrhythmias detection and classification model has proven superior performance over existing approaches.
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Bhukya, R., Shastri, R., Chandurkar, S.S. et al. Detection and classification of cardiac arrhythmia using artificial intelligence. Int J Syst Assur Eng Manag (2023). https://doi.org/10.1007/s13198-023-02035-7
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DOI: https://doi.org/10.1007/s13198-023-02035-7