Association Rule Mining for Predicting Heart Disease: Challenges and Opportunities

Year : 2024 | Volume : 11 | Issue : 03 | Page : 29 34
    By

    Chaman Singh Ahirwar,

  • Vivek Sharma,

  1. Scholar, Department of Computer Science and Engineering, Technocrats Institute of Technology College Bhopal, Bhopal, Madhya Pradesh, India
  2. Professor, Department of Computer Science and Engineering, Technocrats Institute of Technology College Bhopal, Bhopal, Madhya Pradesh, India

Abstract

The exponential growth of digital healthcare data has spurred innovative applications of data mining techniques in medical research and practice. Among these, association rule mining stands out for its ability to uncover meaningful correlations within diverse datasets, such as electronic health records, imaging data, and genetic information. This paper reviews the application of association rule mining in predicting heart diseases, emphasizing its potential to enhance early detection, risk stratification, and personalized treatment. It discusses key challenges in mining association rules from distributed medical databases, including data heterogeneity, privacy concerns, scalability issues, data quality, and the complexity of medical knowledge representation. Furthermore, the paper explores how the integration of advanced machine learning algorithms can refine the predictive power of association rule mining. Addressing these challenges is essential for leveraging association rule mining to improve predictive modeling and advance personalized medicine in cardiovascular health, ultimately leading to more accurate diagnoses and better patient outcomes.

Keywords: Association rule mining, heart disease prediction, digital healthcare data, data mining challenges, personalized medicine

[This article belongs to Journal of Advanced Database Management & Systems ]

How to cite this article:
Chaman Singh Ahirwar, Vivek Sharma. Association Rule Mining for Predicting Heart Disease: Challenges and Opportunities. Journal of Advanced Database Management & Systems. 2024; 11(03):29-34.
How to cite this URL:
Chaman Singh Ahirwar, Vivek Sharma. Association Rule Mining for Predicting Heart Disease: Challenges and Opportunities. Journal of Advanced Database Management & Systems. 2024; 11(03):29-34. Available from: https://journals.stmjournals.com/joadms/article=2024/view=177254


References

  1. Budaraju RR, Jammalamadaka SKR. Mining negative associations from medical databases considering frequent, regular, closed and maximal patterns. Computers. 2024;13:18. doi: 10.3390/computers13010018.
  2. Hosseinkhah F, Ashktorab H, Veen R. Challenges in data mining on medical databases. In: Database Technologies. IGI Global; 2009. p. 1393–1404.
  3. Guzzi PH, Milano M, Cannataro M. Mining association rules from gene ontology and protein networks: promises and challenges. Procedia Computer Science. 2014;29:1970–1980. doi: 10.1016/j.procs.2014.05.181.
  4. Ramasamy S, Nirmala K. Disease prediction in data mining using association rule mining and keyword based clustering algorithms. Int J Comput Appl. 2020;42:1–8. doi: 10.1080/1206212X.2017.1396415.
  5. Rashid MA, Hoque MT, Sattar A. Association rules mining based clinical observations. arXiv Preprint ArXiv:1401.2571; 2014.
  6. Kuba HA, Azzawi MA, Darwish SM, Hassen OA, Abdulhussein AA. An adaptive privacy preserving framework for distributed association rule mining in healthcare databases. Comput Mater Contin. 2023;74:4119–4133. doi: 10.32604/cmc.2023.033182.
  7. Khedr AM, Aghbari ZA, Ali AA, Eljamil M. An efficient association rule mining from distributed medical databases for predicting heart diseases. IEEE Access. 2021;9:15320–15333. doi: 10.1109/ACCESS.2021.3052799.
  8. Yadav C, Lade S, Suman MK. Predictive analysis for the diagnosis of coronary artery disease using association rule mining. Int J Comput Appl. 2014;87:9–13. doi: 10.5120/15195-3575.
  9. Moradi M, Keyvanpour MR. An analytical review of XML association rules mining. Artif Intell Rev. 2015;43:277–300. doi: 10.1007/s10462-012-9376-5.
  10. Xu W, Zhao Q, Zhan Y, Wang B, Hu Y. Privacy-preserving association rule mining based on electronic medical system. Wireless Networks. 2022;28:303–317. doi: 10.1007/s11276-021-0
    2846-1.
  11. Dangare CS, Apte SS. Improved study of heart disease prediction system using data mining classification techniques. Int J Comput Appl. 2012;47:44–48. doi: 10.5120/7228-0076.
  12. Vijiyarani S, Sudha S. Disease prediction in data mining technique–a survey. Int J Comput Appl Inf Technol. 2013;2:17–21.
Regular Issue Subscription Review Article
Volume 11
Issue 03
Received 28/08/2024
Accepted 26/09/2024
Published 07/10/2024
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