Disease risk prediction by mining personalized health trend patterns: A case study on diabetes

Guo Cheng Lan, Chao Hui Lee, Yu Yen Lee, Vincent S. Tseng, Chu Yu Chin, Miin Luen Day, Shyh Chyi Wang, Ching Nain Chang, Shyr Yuan Cheng, Jin Shang Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Health examination has played an important role for maintaining people's health since it can not only help people understand their own health conditions clearly but also avoid missing the best timing of disease treatment. However, in current health examination systems, people get only a basic report from single health examination and no advanced health risk analysis is provided. In this paper, we proposed an effective mechanism for chronic disease risk prediction by mining the data containing historical health records and personal life style information. Value change trends of the data are important for disease status prediction, and we defined significant ones as health risk patterns in our mechanism. Risks of a chronic disease can be predicted early with a mechanism built with our health risk patterns and it also proven work well through experimental evaluations on real datasets. Our method outperformed traditional mechanism in terms of accuracy, precision and sensitivity for predicting the risk of diabetes. In particular, insightful observations show that the consideration of life-style information can effectively enhance whole performance for risk prediction. Moreover, classification rules produced by our mechanism which integrates C4.5 and CBA provide physicians disease related health risk patterns such that appropriate treatments could be given to people for disease prevention.

Original languageEnglish
Title of host publicationProceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012
Pages27-32
Number of pages6
DOIs
Publication statusPublished - 2012 Dec 1
Event2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012 - Tainan, Taiwan
Duration: 2012 Nov 162012 Nov 18

Publication series

NameProceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012

Other

Other2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012
CountryTaiwan
CityTainan
Period12-11-1612-11-18

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Lan, G. C., Lee, C. H., Lee, Y. Y., Tseng, V. S., Chin, C. Y., Day, M. L., Wang, S. C., Chang, C. N., Cheng, S. Y., & Wu, J. S. (2012). Disease risk prediction by mining personalized health trend patterns: A case study on diabetes. In Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012 (pp. 27-32). [6395001] (Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012). https://doi.org/10.1109/TAAI.2012.53