The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Enhancing Outlier Detection in Healthcare Data through Mahalanobis Distance Metric Analysis
Abstract
Mahalanobis distance is a useful multivariate statistic for determining how far apart two points are from one another. It is a very helpful statistic with excellent uses in multivariate anomaly detection, one-class classification, and classification on severely unbalanced datasets.This compilation delves into the refinement of outlier detection within healthcare data by employing Mahalanobis Distance Metric Analysis as its core methodology. Using Pure Tone Audiometry threshold data as a case study, the research highlights the method's effectiveness in identifying and characterizing outliers. The emphasis is placed on the robustness and applicability of the Mahalanobis Distance Metric, showcasing its potential to enhance outlier detection methodologies across diverse healthcare datasets. This study contributes a methodological advancement that extends beyond the specific application to Pure Tone Audiometry, offering a versatile framework for improved outlier detection in various healthcare domains.
Related Content
|
V. Leela, R. Sangeetha, S. Geetha, B. Deepa.
© 2026.
38 pages.
|
|
A Prabhu Chakkaravarthy, Dhanalakshmi Jaganathan.
© 2026.
20 pages.
|
|
Hasini Balage, Darshana Sedera.
© 2026.
24 pages.
|
|
Dilek Gümüş.
© 2026.
34 pages.
|
|
Fawaz Azizieh, Bulent Yilmaz.
© 2026.
46 pages.
|
|
Kutay Icoz.
© 2026.
54 pages.
|
|
Rajganesh Nagarajan, G. Kavitha.
© 2026.
36 pages.
|
|
|