Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6857664 | Information Sciences | 2015 | 21 Pages |
Abstract
This paper presents a new data mining environment targeted to patient data analysis. It tackles the issue of extracting generalized rules from weighted patient data, where items may weight differently according to their importance within each transaction. To this aim, it proposes a novel type of association rule, namely the Weighted Generalized Association Rule (W-GAR). The usefulness of the proposed pattern has been evaluated on real patient datasets equipped with a taxonomy built over examinations and drugs. The achieved results demonstrate the effectiveness of the proposed approach in mining interesting and actionable knowledge in a real medical care scenario.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Elena Baralis, Luca Cagliero, Tania Cerquitelli, Silvia Chiusano, Paolo Garza,