Data-Mining-in-Healthcare
Authors (HEADS-INFORM 2025):
- Henrique Pereira
- Olívia Oliveira
Introduction
Data mining refers to the process of discovering meaningful patterns and extracting actionable knowledge from vast amounts of data, often with the aid of computational tools.
The healthcare sector generates massive amounts of data, including electronic health records (EHRs), genomic sequences, medical imaging, wearable device outputs, patient-generated data, and clinical trial data. These data sources provide a fertile ground for applying data mining to improve care delivery, patient outcomes, and operational efficiency (Islam et al., 2018 [1]).
Importance:
- Enhances clinical decision-making;
- Facilitates the early detection and prediction of diseases;
- Improves operational efficiencies in healthcare systems;
- Supports public health research and epidemiological studies.
The image above is presented in Gordan et al., 2022 [2], and illustrates a historical timeline of advancements in Structural Health Monitoring (SHM) and its integration with Artificial Intelligence (AI) and Data Mining technologies.
Key Data Mining Techniques in Healthcare
Classification
Regression
Clustering
Anomaly Detection
Association Rules
Neural Networks and Deep Learning
Applications of Data Mining in Healthcare
Challenges in Healthcare Data Mining
Future Directions of Data Mining in Healthcare
References
- ↑ Islam MS, Hasan MM, Wang X, Germack HD, Noor-E-Alam M. A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining. Healthcare. 2018 May 23;6(2):54.
- ↑ Gordan M, Sabbagh-Yazdi SR, Ismail Z, Ghaedi K, Carroll P, McCrum D, et al. State-of-the-art review on advancements of data mining in structural health monitoring. Measurement. 2022 Apr 1;193:110939.
