10.57647/j.fomj.2025.8515

Early prediction of Cardiac Arrhythmia based on Active Fuzzy Deep Learning

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  • Homeyra Amiri1,2,
  • Javad Mohammadzadeh*1,2,
  • Seyed Mohsen Mirhosseini1,2,
  • Alireza Nikravanshelmani3,4,
  1. Department of Computer Engineering
  2. Institute of Artificial Intelligence and Social and Advanced Technology, Ka.C., Islamic Azad University, Karaj, Iran
  3. Department of Computer Engineering
  4. Institute of Artificial Intelligence and Social and Advanced Technology, Ka.C., Islamic Azad University, Karaj, Iran

Received: 2025-01-28

Revised: 2025-03-15

Accepted: 2025-03-28

Published in Issue 2025-04-23

Copyright (c) 2025 Homeyra Amiri, Javad Mohammadzadeh, Seyed Mohsen Mirhosseini, Alireza Nikravanshelmani (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Amiri, H., Mohammadzadeh, J., Mirhosseini, S. M. ., & Nikravanshelmani, A. (2025). Early prediction of Cardiac Arrhythmia based on Active Fuzzy Deep Learning. Fuzzy Optimization and Modeling Journal (FOMJ), 6(1). https://doi.org/10.57647/j.fomj.2025.8515
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Abstract

In the contemporary era, numerous challenges encountered by humanity can be mitigated through the integration of computer science and artificial intelligence.  A plethora of diseases that result in human mortality can be predicted and treated with artificial intelligence. In the field of cardiovascular diseases, cardiac arrhythmia has been identified as one of the most prevalent conditions, as evidenced by the findings of this research study. Cardiac arrhythmias are categorised into distinct types, with varying levels of safety and risk. This article proposes a methodology that utilises advanced artificial intelligence techniques to forecast high-risk cardiac arrhythmias in individuals. This article proposes a novel methodology that integrates deep learning, fuzzy logic and active learning to facilitate early prediction of cardiac arrhythmias. The proposed approach, termed CA-DAL (Cardiac Arrhythmia Early Prediction using Deep Active Learning), employs a multifaceted integration of these advanced techniques to enhance the accuracy and efficiency of arrhythmia detection. The proposed method has been compared with nine other algorithms that deal with data classification, which includes traditional and modern methods, and has provided significant and comparable results. In conclusion, the CA-DAL demonstrates a promising advancement in the early prediction of high-risk arrhythmia, effectively outperforming traditional and modern algorithms in data classification. This innovative approach underscores the potential of artificial intelligence to enhance disease prediction and treatment, thereby contributing to enhanced healthcare outcomes.

Keywords

  • Active Learning Deep Learning Fuzzy logic Cardiac Arrhythmia Machine learning Artificial Intelligence
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