Chaman Singh Ahirwar,
Vivek Sharma,
- M. Tech Scholar, Department of Computer Science and Engineering, Technocrats Institute of Technology College Bhopal, Bhopal, Madhya Pradesh, India
- 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 (joadms)]
Chaman Singh Ahirwar, Vivek Sharma. Association Rule Mining for Predicting Heart Disease: Challenges and Opportunities. Journal of Advanced Database Management & Systems. 2024; 11(03):-.
Chaman Singh Ahirwar, Vivek Sharma. Association Rule Mining for Predicting Heart Disease: Challenges and Opportunities. Journal of Advanced Database Management & Systems. 2024; 11(03):-. Available from: https://journals.stmjournals.com/joadms/article=2024/view=177254
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Journal of Advanced Database Management & Systems
| Volume | 11 |
| Issue | 03 |
| Received | 28/08/2024 |
| Accepted | 26/09/2024 |
| Published | 07/10/2024 |
