@inproceedings{65258ace6ad84fc5b59c0738efbcff17,
title = "A chronic disease analysis system based on dirty data mining",
abstract = "With the rapid progress in data mining techniques, more and more systems are facing to the analysis of chronic disease because of the convenience for doctors and patients. However, low-quality data seriously leads to low-quality analysis results which may cause one{\textquoteright}s life lost. Even though many efforts have been made to enhance data quality, there always exists the data which we cannot get the exact value. Motivated by this, we develop a chronic disease analysis system adopting the mechanism that combines data cleaning and fault-tolerant data mining. In our system, we conduct a complete data mining of raw dirty data set and integrate the analysis of some kinds of chronic disease which is different to just analysis for a single disease. Moreover, our system also provides a platform for training and testing a new medical data set which is more convenient for users who do not know data mining well.",
author = "Ming Sun and Hongzhi Wang and Jianzhong Li and Hong Gao and Shenbin Huang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.",
year = "2016",
doi = "10.1007/978-3-319-45817-5\_63",
language = "英语",
isbn = "9783319458168",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "552--555",
editor = "Kyuseok Shim and Kai Zheng and Guanfeng Liu and Feifei Li",
booktitle = "Web Technologies and Applications - 18th Asia-Pacific Web Conference, APWeb 2016, Proceedings",
address = "德国",
}