Preventing Cardiac Arrest Using Novel Clustering Technique With Bio-Inspired Optimization Algorithm

Authors

  • Jayachandran A. Professor & HOD, Department of Computer Science and Engineering, Presidency University, Bangalore, India
  • Mohammad Asif Iqbal Assistant Professor, Department of Electrical Engineeing, Vivekananda Global University, Jaipur, India
  • Anu Sharma Assistant Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
  • Kumod Kumar Gupta Assistant Professor, Department of Artificial Intelligence (AI), Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India

Keywords:

Cardiac arrest prediction, Bio-inspired algorithm, Optimization and Clustering

Abstract

Cardiac illness is the most infectious disease in the world currently for individuals of all ages. An essential necessity to anticipate heart illness correctly in a short period. Problems that are both complex and persistent are best tackled using optimization methods. The majority of applications of machine learning and clustering techniques are in the field of cardiovascular disease prediction. When making predictions, clustering makes heavy use of classification algorithms. For data preparation and cleaning, the hamming distance feature selection approach is suggested in this article for use across various heart illness datasets. In order to provide a reliable forecast of heart illness, a bio-inspired clustering model like Bilinear Fuzzy K-means Clustering (BFKC) is used with the Chaotic Drift Cuckoo Search Optimization Algorithm (CDCSOA). The findings show that BFKC-trained CDCSOA performs well, with an accuracy of 95 percent.

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Published

11.07.2023

How to Cite

A., J. ., Iqbal, M. A. ., Sharma, A. ., & Gupta, K. K. . (2023). Preventing Cardiac Arrest Using Novel Clustering Technique With Bio-Inspired Optimization Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 414–422. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3068

Issue

Vol. 11 No. 8s (2023): Advances on Machine Learning and Artificial Intelligence in Computer Technology

Section

Research Article

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