A Comparison of Machine Learning Algorithms for Multilabel Classification of CAN

Herbert Jelinek; A.V. Kelarev; A. Stranieri; J.L. Yearwood

A Comparison of Machine Learning Algorithms for Multilabel Classification of CAN

Herbert Jelinek, A.V. Kelarev, A. Stranieri, J.L. Yearwood

Research output: Contribution to journal › Article › peer-review

Abstract

This article is devoted to the investigation and comparison of several important machine learning algorithms in their ability to obtain multilabel classifications of the stages of cardiac autonomic neuropathy (CAN). Data was collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments have achieved better results than those published previously in the literature for similar CAN identification tasks.

Original language	English
Pages (from-to)	1-4
Number of pages	4
Journal	Advances in Computer Science and Engineering
Volume	9
Issue number	1
Publication status	Published - 2012

Cite this

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AB - This article is devoted to the investigation and comparison of several important machine learning algorithms in their ability to obtain multilabel classifications of the stages of cardiac autonomic neuropathy (CAN). Data was collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments have achieved better results than those published previously in the literature for similar CAN identification tasks.

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A Comparison of Machine Learning Algorithms for Multilabel Classification of CAN

Abstract

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