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Prediction of diabetes mellitus based on boosting ensemble modeling
conference contribution
posted on 2023-05-23, 09:54 authored by Ali, R, Siddiqi, MH, Idris, M, Byeong KangByeong Kang, Lee, SHealthcare 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.
History
Publication title
Lecture Notes in Computer Science 8867: Proceedings of the 8th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2014)Volume
8867Editors
R Hervas, S Lee, C Nugent, J BravoPagination
25-28ISSN
0302-9743Department/School
School of Information and Communication TechnologyPublisher
IEEE - Inst Electrical Electronics Engineers IncPlace of publication
New York, USAEvent title
8th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2014)Event Venue
Belfast, UKDate of Event (Start Date)
2014-12-02Date of Event (End Date)
2014-12-05Rights statement
Copyright 2014 Springer International Publishing SwitzerlandRepository Status
- Restricted