International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Anomaly Detection in Healthcare Data Warehouses using ML

Author(s) Arun Kumar Ramachandran Sumangala Devi
Country United States
Abstract Fast emergence of digitalization leads to unprecedented growth in the generation of the healthcare sector-particularly EHRs and medical equipment data. This extended the way for challenges for integrity in managing data and anomaly detection, including fraudulent transactions, medication errors, and many more system failures. Modern healthcare data poses a challenge to traditional methods of anomaly detection due to high and complex dimensionality. Machine learning provides a strong solution, using algorithms such as Gaussian Mixture Models, One-Class SVM and deep learning algorithms such as Autoencoders, and Recurrent Neural Networks in the detection of anomalies in healthcare data warehouse settings [1]. This study reports how ML can help advance care for patients, enable the validity of the data and reduce costs through real-time monitoring, fraud detection, and early detection of diseases. Applying anomaly detection through ML would most likely bring better operational performance, patient safety, and decision-making in health care for organizations as issues of poor data quality, lack of interpretability of models, and real-time detection would be addressed .
Keywords Machine learning, fraud detection healthcare data warehouses, anomaly detection, patient safety, operational efficiency.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 5, September-October 2024
Published On 2024-10-14
Cite This Anomaly Detection in Healthcare Data Warehouses using ML - Arun Kumar Ramachandran Sumangala Devi - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.28705
DOI https://doi.org/10.36948/ijfmr.2024.v06i05.28705
Short DOI https://doi.org/g8k5w9
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