| Title: | Comparative Analysis of Unsupervised Concept Drift Detection Techniques in High-Dimensional Biomedical Data Streams |
| Journal: | International Journal of Scientific Research in Computer Science Engineering and Information Technology |
| Published: | 14 May 2025 |
| DOI: | https://doi.org/10.32628/cseit25113302 |
| URL: | https://www.ijsrcseit.com/index.php/home/article/download/CSEIT25113302/CSEIT25113302 |
Publication 15375
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Abstract
In the area of real-time analytics, the ability to detect concept drift shifts in data distribution over time is vital for maintaining the reliability of predictive models. This analysis presents a comprehensive comparative analysis for five Unsupervised Concept Drift Detection Algorithms Adaptive Boosting (AdaBoost), Diversity-Induced Ensemble (DIE), Adaptive Sliding Window (ADWIN), Sequential Probability Ratio Test (SPRT), and Page-Hinkley Test (PHT) with a focus on high-dimensional biomedical data streams. The evaluation is conducted using three large-scale and diverse biomedical datasets: MIMIC-III/IV, UK Biobank, and MedMNIST, each representing a distinct challenge in terms of dimensionality, temporal variability and data type (tabular, genomic, and imaging). Performance is assessed across key metrics including Detection Delay, Memory Usage, Execution Time, and post-drift classification effectiveness (Precision, Recall, F1-Score, and Accuracy). Both synthetic and real-world drifts are incorporated to simulate dynamic environments. The findings reveal that ensemble-based methods such as AdaBoost and DIE outperform statistical approaches in handling noisy, sparse, and high-dimensional streams, offering superior adaptability and robustness. This research contributes a systematic evaluation framework and empirical insights to guide the deployment of unsupervised drift-aware systems in healthcare and other data-intensive domains.</p>
2 Authors
- Priyanka Rajamani
- Dr. J. Savitha
Enabling scientific discoveries that improve human health