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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, S
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.

History

Publication title

Lecture Notes in Computer Science 8867: Proceedings of the 8th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2014)

Volume

8867

Editors

R Hervas, S Lee, C Nugent, J Bravo

Pagination

25-28

ISSN

0302-9743

Department/School

School of Information and Communication Technology

Publisher

IEEE - Inst Electrical Electronics Engineers Inc

Place of publication

New York, USA

Event title

8th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2014)

Event Venue

Belfast, UK

Date of Event (Start Date)

2014-12-02

Date of Event (End Date)

2014-12-05

Rights statement

Copyright 2014 Springer International Publishing Switzerland

Repository Status

  • Restricted

Socio-economic Objectives

Information services not elsewhere classified

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    University Of Tasmania

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