Abstract
Machine learning and artificial intelligence have a wide range of applications in medical domain, such as detecting anomalous reading, anomalous patient health condition, etc. Many algorithms have been developed to solve this problem. However, they fail to answer why those entries are considered as an outlier. This research gap leads to outlying aspect mining problem. The problem of outlying aspect mining aims to discover the set of features (a.k.a subspace) in which the given data point is dramatically different than others. In this paper, we present an interesting application of outlying aspect mining in the medical domain. This paper aims to effectively and efficiently identify outlying aspects using different outlying aspect mining algorithms and evaluate their performance on different real-world healthcare datasets. The experimental results show that the latest isolation-based outlying aspect mining measure, SiNNE, have outstanding performance on this task and have promising results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Anomaly and outlier are most commonly used terms in the literature. In this work, hereafter, we will use outlier term only.
- 2.
The description of data set is provided in Table 1.
- 3.
Available at https://www.ipd.kit.edu/~muellere/HiCS/.
- 4.
- 5.
Due to space limitation, we only present discovered subspaces of two queries. We choose queries where discovered subspaces are different for each scoring measure.
- 6.
RBeam and Beam are unable to finish the process in 1Â h for Annthyroid data set.
References
Bandaragoda, T.R., Ting, K.M., Albrecht, D., Liu, F.T., Wells, J.R.: Efficient anomaly detection by isolation using nearest neighbour ensemble. In: 2014 IEEE International Conference on Data Mining Workshop, pp. 698–705 (2014). https://doi.org/10.1109/ICDMW.2014.70
Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93–104. SIGMOD 2000, Association for Computing Machinery, New York, NY, USA (2000). https://doi.org/10.1145/342009.335388
Campos, G.O., et al.: On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining Knowl. Disc. 30(4), 891–927 (2016). https://doi.org/10.1007/s10618-015-0444-8
Duan, L., Tang, G., Pei, J., Bailey, J., Campbell, A., Tang, C.: Mining outlying aspects on numeric data. Data Mining Knowl. Disc. 29(5), 1116–1151 (2015). https://doi.org/10.1007/s10618-014-0398-2
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009). https://doi.org/10.1145/1656274.1656278
Härdle, W.: Smoothing techniques: with implementation in S. Springer Science & Business Media, New York (2012)
Keller, F., Muller, E., Bohm, K.: Hics: high contrast subspaces for density-based outlier ranking. In: 2012 IEEE 28th International Conference on Data Engineering, pp. 1037–1048 (2012). https://doi.org/10.1109/ICDE.2012.88
Laurikkala, J., Juhola, M., Kentala, E., Lavrac, N., Miksch, S., Kavsek, B.: Informal identification of outliers in medical data. In: Fifth International Workshop on Intelligent Data Analysis in Medicine and Pharmacology, Vol. 1, pp. 20–24 (2000)
Pham, T.D.: Classification of COVID-19 chest x-rays with deep learning: new models or fine tuning? Health Inf. Sci. Syst. 9(1), 1–11 (2021)
Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: A brain tumor segmentation framework based on outlier detection. Med. Image Anal. 8(3), 275–283 (2004). https://doi.org/10.1016/j.media.2004.06.007
Samariya, D., Aryal, S., Ting, K.M., Ma, J.: A new effective and efficient measure for outlying aspect mining. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds.) WISE 2020. LNCS, vol. 12343, pp. 463–474. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62008-0_32
Scott, D.W.: Multivariate Density Estimation: Theory, Practice, and Visualization. John Wiley & Sons, Hoboken (2015)
Tachmazidis, I., Chen, T., Adamou, M., Antoniou, G.: A hybrid AI approach for supporting clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in adults. Health Inf. Syst. 9(1), 1–8 (2021)
van Capelleveen, G., Poel, M., Mueller, R.M., Thornton, D., van Hillegersberg, J.: Outlier detection in healthcare fraud: a case study in the Medicaid dental domain. Int. J. Acc. Inf. Syst. 21, 18–31 (2016). https://doi.org/10.1016/j.accinf.2016.04.001
Vinh, N.X., Chan, J., Romano, S., Bailey, J., Leckie, C., Ramamohanarao, K., Pei, J.: Discovering outlying aspects in large datasets. Data Mining Knowl. Disc. 30(6), 1520–1555 (2016). https://doi.org/10.1007/s10618-016-0453-2
Wells, J.R., Ting, K.M.: A new simple and efficient density estimator that enables fast systematic search. Pattern Recogn. Lett. 122, 92–98 (2019). https://doi.org/10.1016/j.patrec.2018.12.020
Acknowledgement
This work is supported by Federation University Research Priority Area (RPA) scholarship, awarded to Durgesh Samariya. We are thankful to the anonymous reviewers for their critical comments to improve the quality of the paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Samariya, D., Ma, J. (2021). Mining Outlying Aspects on Healthcare Data. In: Siuly, S., Wang, H., Chen, L., Guo, Y., Xing, C. (eds) Health Information Science. HIS 2021. Lecture Notes in Computer Science(), vol 13079. Springer, Cham. https://doi.org/10.1007/978-3-030-90885-0_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-90885-0_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-90884-3
Online ISBN: 978-3-030-90885-0
eBook Packages: Computer ScienceComputer Science (R0)