Prediction of diabetes mellitus based on boosting ensemble modeling

被引：14

作者：

Ali, Rahman ^{[1
]}

Siddiqi, Muhammad Hameed ^{[1
]}

Idris, Muhammad ^{[1
]}

Kang, Byeong Ho ^{[2
]}

Lee, Sungyoung ^{[1
]}

机构：

[1] Dept. of Computer Engineering, Kyung Hee University, Korea, Republic of

[2] Dept. of Engineering and Technology, ICT, University of Tasmania, Australia

来源：

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2014年 / 8867卷

关键词：

Forecasting - Knowledge acquisition;

D O I：

10.1007/978-3-319-13102-3_6

中图分类号：

学科分类号：

摘要：

Healthcare systems provide personalized services in wide spread domains to help patients in fitting themselves into their normal activities of life. This study is focused on the prediction of diabetes types of patients based on their personal and clinical information using a boosting ensemble technique that internally uses random committee classifier. To evaluate the technique, a real set of data containing 100 records is used. The prediction accuracy obtained is 81.0% based on experiments performed in Weka with 10-fold cross validation. © Springer International Publishing Switzerland 2014.

引用

页码：25 / 28