Hypoglycemia detection using fuzzy inference system with genetic algorithm

Sai Ho Ling; Hung T. Nguyen; Hung Fat Frank Leung

doi:10.1109/FUZZY.2011.6007319

Hypoglycemia detection using fuzzy inference system with genetic algorithm

Sai Ho Ling, Hung T. Nguyen, Hung Fat Frank Leung

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

3 Citations (Scopus)

Abstract

In this paper, we develope a genetic algorithm based fuzzy inference system to recognize hypoglycemic episodes based on heart rate and corrected QT interval of the electrocardiogram (ECG) signal. Genetic algorithm is introduced to optimize the membership functions and fuzzy rules. A practical experiment based on data from 15 children with T1DM is studied. All the data sets are collected from the Department of Health, Government of Western Australia. To prevent the phenomenon of overtraining (over-fitting), a validation strategy that may adjust the fitness function is proposed. Thus, the data are organized into a training set, a validation set, and a testing set randomly selected. The classification results in term of sensitivity, specificity, and receiver operating characteristic (ROC) analysis show that the proposed classification method performs well.

Original language	English
Title of host publication	FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings
Pages	2225-2231
Number of pages	7
DOIs	https://doi.org/10.1109/FUZZY.2011.6007319
Publication status	Published - 27 Sept 2011
Event	2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei, Taiwan Duration: 27 Jun 2011 → 30 Jun 2011

Conference

Conference	2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011
Country/Territory	Taiwan
City	Taipei
Period	27/06/11 → 30/06/11

Keywords

Diabetes
Fuzzy logic
Genetic algorithm
Hypoglycemia

ASJC Scopus subject areas

Theoretical Computer Science
Software
Artificial Intelligence
Applied Mathematics

UN SDGs

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

More information

10.1109/FUZZY.2011.6007319

Cite this

@inproceedings{f3de54cd43594ce88be66cc7153a712d,

title = "Hypoglycemia detection using fuzzy inference system with genetic algorithm",

abstract = "In this paper, we develope a genetic algorithm based fuzzy inference system to recognize hypoglycemic episodes based on heart rate and corrected QT interval of the electrocardiogram (ECG) signal. Genetic algorithm is introduced to optimize the membership functions and fuzzy rules. A practical experiment based on data from 15 children with T1DM is studied. All the data sets are collected from the Department of Health, Government of Western Australia. To prevent the phenomenon of overtraining (over-fitting), a validation strategy that may adjust the fitness function is proposed. Thus, the data are organized into a training set, a validation set, and a testing set randomly selected. The classification results in term of sensitivity, specificity, and receiver operating characteristic (ROC) analysis show that the proposed classification method performs well.",

keywords = "Diabetes, Fuzzy logic, Genetic algorithm, Hypoglycemia",

author = "Ling, {Sai Ho} and Nguyen, {Hung T.} and Leung, {Hung Fat Frank}",

year = "2011",

month = sep,

day = "27",

doi = "10.1109/FUZZY.2011.6007319",

language = "English",

isbn = "9781424473175",

pages = "2225--2231",

booktitle = "FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings",

note = "2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 ; Conference date: 27-06-2011 Through 30-06-2011",

}

Hypoglycemia detection using fuzzy inference system with genetic algorithm. / Ling, Sai Ho; Nguyen, Hung T.; Leung, Hung Fat Frank.
FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings. 2011. p. 2225-2231 6007319.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Hypoglycemia detection using fuzzy inference system with genetic algorithm

AU - Ling, Sai Ho

AU - Nguyen, Hung T.

AU - Leung, Hung Fat Frank

PY - 2011/9/27

Y1 - 2011/9/27

N2 - In this paper, we develope a genetic algorithm based fuzzy inference system to recognize hypoglycemic episodes based on heart rate and corrected QT interval of the electrocardiogram (ECG) signal. Genetic algorithm is introduced to optimize the membership functions and fuzzy rules. A practical experiment based on data from 15 children with T1DM is studied. All the data sets are collected from the Department of Health, Government of Western Australia. To prevent the phenomenon of overtraining (over-fitting), a validation strategy that may adjust the fitness function is proposed. Thus, the data are organized into a training set, a validation set, and a testing set randomly selected. The classification results in term of sensitivity, specificity, and receiver operating characteristic (ROC) analysis show that the proposed classification method performs well.

AB - In this paper, we develope a genetic algorithm based fuzzy inference system to recognize hypoglycemic episodes based on heart rate and corrected QT interval of the electrocardiogram (ECG) signal. Genetic algorithm is introduced to optimize the membership functions and fuzzy rules. A practical experiment based on data from 15 children with T1DM is studied. All the data sets are collected from the Department of Health, Government of Western Australia. To prevent the phenomenon of overtraining (over-fitting), a validation strategy that may adjust the fitness function is proposed. Thus, the data are organized into a training set, a validation set, and a testing set randomly selected. The classification results in term of sensitivity, specificity, and receiver operating characteristic (ROC) analysis show that the proposed classification method performs well.

KW - Diabetes

KW - Fuzzy logic

KW - Genetic algorithm

KW - Hypoglycemia

UR - http://www.scopus.com/inward/record.url?scp=80053088256&partnerID=8YFLogxK

U2 - 10.1109/FUZZY.2011.6007319

DO - 10.1109/FUZZY.2011.6007319

M3 - Conference article published in proceeding or book

SN - 9781424473175

SP - 2225

EP - 2231

BT - FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings

T2 - 2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011

Y2 - 27 June 2011 through 30 June 2011

ER -

Hypoglycemia detection using fuzzy inference system with genetic algorithm

Abstract

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

More information

Other files and links

Fingerprint

Cite this