Bagging model with cost sensitive analysis on diabetes data

Punnee Sittidech; Nongyao Nai-arun; Ian T. Nabney

Bagging model with cost sensitive analysis on diabetes data

Punnee Sittidech
, Nongyao Nai-arun
, Ian T. Nabney

Nakhon Sawan Rajabhat University
Naresuan University

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

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Abstract

Diabetes patients might suffer from an unhealthy life, long-term treatment and chronic complicated diseases. The decreasing hospitalization rate is a crucial problem for health care centers. This study combines the bagging method with base classifier decision tree and costs-sensitive analysis for diabetes patients' classification purpose. Real patients' data collected from a regional hospital in Thailand were analyzed. The relevance factors were selected and used to construct base classifier decision tree models to classify diabetes and non-diabetes patients. The bagging method was then applied to improve accuracy. Finally, asymmetric classification cost matrices were used to give more alternative models for diabetes data analysis.

Original language	English
Pages (from-to)	82-90
Number of pages	9
Journal	Information Technology Journal
Volume	11
Issue number	1
Publication status	Published - 2015

Bibliographical note

This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

diabetes
feature selection
classification
bagging
cost-sensitive analysis

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Bagging Model with Cost Sensitive Analysis on Diabetes Data
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
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Cite this

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N2 - Diabetes patients might suffer from an unhealthy life, long-term treatment and chronic complicated diseases. The decreasing hospitalization rate is a crucial problem for health care centers. This study combines the bagging method with base classifier decision tree and costs-sensitive analysis for diabetes patients' classification purpose. Real patients' data collected from a regional hospital in Thailand were analyzed. The relevance factors were selected and used to construct base classifier decision tree models to classify diabetes and non-diabetes patients. The bagging method was then applied to improve accuracy. Finally, asymmetric classification cost matrices were used to give more alternative models for diabetes data analysis.

AB - Diabetes patients might suffer from an unhealthy life, long-term treatment and chronic complicated diseases. The decreasing hospitalization rate is a crucial problem for health care centers. This study combines the bagging method with base classifier decision tree and costs-sensitive analysis for diabetes patients' classification purpose. Real patients' data collected from a regional hospital in Thailand were analyzed. The relevance factors were selected and used to construct base classifier decision tree models to classify diabetes and non-diabetes patients. The bagging method was then applied to improve accuracy. Finally, asymmetric classification cost matrices were used to give more alternative models for diabetes data analysis.

KW - diabetes

KW - feature selection

KW - classification

KW - bagging

KW - cost-sensitive analysis

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JO - Information Technology Journal

JF - Information Technology Journal

IS - 1

ER -

Bagging model with cost sensitive analysis on diabetes data

Abstract

Bibliographical note

UN SDGs

Keywords

Access to Document

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