Prediction of Diabetes Mellitus Based on Boosting Ensemble Modeling

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 techni...

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Bibliographic Details
Published inUbiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services pp. 25 - 28
Main Authors Ali, Rahman, Siddiqi, Muhammad Hameed, Idris, Muhammad, Kang, Byeong Ho, Lee, Sungyoung
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing 2014
SeriesLecture Notes in Computer Science
Subjects
Boosting
Diabetes
Ensemble Modeling
Knowledge Acquisition
Prediction
Reasoning
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ISBN9783319131016
331913101X
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-13102-3_6

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Summary: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.
ISBN:9783319131016
331913101X
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-13102-3_6