Hybrid Efficient Unsupervised Anomaly Detection for Early Pandemic Case Identification
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908348124758016 |
|---|---|
| author | Ghajari, Ghazal PK, Mithun Kumar Amsaad, Fathi |
| author_facet | Ghajari, Ghazal PK, Mithun Kumar Amsaad, Fathi |
| contents | Unsupervised anomaly detection is a promising technique for identifying unusual patterns in data without the need for labeled training examples. This approach is particularly valuable for early case detection in epidemic management, especially when early-stage data are scarce. This research introduces a novel hybrid method for anomaly detection that combines distance and density measures, enhancing its applicability across various infectious diseases. Our method is especially relevant in pandemic situations, as demonstrated during the COVID-19 crisis, where traditional supervised classification methods fall short due to limited data. The efficacy of our method is evaluated using COVID-19 chest X-ray data, where it significantly outperforms established unsupervised techniques. It achieves an average AUC of 77.43%, surpassing the AUC of Isolation Forest at 73.66% and KNN at 52.93%. These results highlight the potential of our hybrid anomaly detection method to improve early detection capabilities in diverse epidemic scenarios, thereby facilitating more effective and timely responses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05347 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hybrid Efficient Unsupervised Anomaly Detection for Early Pandemic Case Identification Ghajari, Ghazal PK, Mithun Kumar Amsaad, Fathi Machine Learning Image and Video Processing Unsupervised anomaly detection is a promising technique for identifying unusual patterns in data without the need for labeled training examples. This approach is particularly valuable for early case detection in epidemic management, especially when early-stage data are scarce. This research introduces a novel hybrid method for anomaly detection that combines distance and density measures, enhancing its applicability across various infectious diseases. Our method is especially relevant in pandemic situations, as demonstrated during the COVID-19 crisis, where traditional supervised classification methods fall short due to limited data. The efficacy of our method is evaluated using COVID-19 chest X-ray data, where it significantly outperforms established unsupervised techniques. It achieves an average AUC of 77.43%, surpassing the AUC of Isolation Forest at 73.66% and KNN at 52.93%. These results highlight the potential of our hybrid anomaly detection method to improve early detection capabilities in diverse epidemic scenarios, thereby facilitating more effective and timely responses. |
| title | Hybrid Efficient Unsupervised Anomaly Detection for Early Pandemic Case Identification |
| topic | Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2408.05347 |