Abstract
The amount of healthcare data is increasing at a rapid pace, and with that is also increasing the need for better and automated analyzes that are able to transform these data into useful knowledge. In turn, this knowledge may bring huge benefits to the healthcare management and leverage the healthcare system in all aspects. In the last decade, data mining has been widely applied to this domain and several approaches, technology and methods were developed for improving decision support. Despite the advances in healthcare mining, the characteristics of these data – complexity, volume, high dimensionality, etc. – still demand more efficient and effective techniques. In this work we present a case study on the healthcare domain. We propose to use a multi-dimensional model of the hepatitis dataset and to apply a multi-dimensional data mining algorithm to find all patterns in the database. These patterns are then used to enrich classification and results show a significant improvement on prediction.
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Silva, A., Antunes, C. (2014). Finding Multi-dimensional Patterns in Healthcare. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2014. Lecture Notes in Computer Science(), vol 8556. Springer, Cham. https://doi.org/10.1007/978-3-319-08979-9_27
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DOI: https://doi.org/10.1007/978-3-319-08979-9_27
Publisher Name: Springer, Cham
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