Version 1
: Received: 26 November 2023 / Approved: 27 November 2023 / Online: 27 November 2023 (05:55:10 CET)
Version 2
: Received: 29 November 2023 / Approved: 30 November 2023 / Online: 30 November 2023 (10:33:48 CET)
How to cite:
Rajamani, S.K.; Iyer, R.S. Enhancing Outlier Detection in Healthcare Data through Mahalanobis Distance Metric Analysis. Preprints2023, 2023111651. https://doi.org/10.20944/preprints202311.1651.v2
Rajamani, S.K.; Iyer, R.S. Enhancing Outlier Detection in Healthcare Data through Mahalanobis Distance Metric Analysis. Preprints 2023, 2023111651. https://doi.org/10.20944/preprints202311.1651.v2
Rajamani, S.K.; Iyer, R.S. Enhancing Outlier Detection in Healthcare Data through Mahalanobis Distance Metric Analysis. Preprints2023, 2023111651. https://doi.org/10.20944/preprints202311.1651.v2
APA Style
Rajamani, S.K., & Iyer, R.S. (2023). Enhancing Outlier Detection in Healthcare Data through Mahalanobis Distance Metric Analysis. Preprints. https://doi.org/10.20944/preprints202311.1651.v2
Chicago/Turabian Style
Rajamani, S.K. and Radha Srinivasan Iyer. 2023 "Enhancing Outlier Detection in Healthcare Data through Mahalanobis Distance Metric Analysis" Preprints. https://doi.org/10.20944/preprints202311.1651.v2
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.
Keywords
Outlier Detection; Mahalanobis Distance Metric Analysis; Healthcare Data; Pure Tone Audiometry; Methodological Advancement; Biostatistics
Subject
Medicine and Pharmacology, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
