Performance Analysis of Machine Learning Algorithms for Hypertension Decision Support System

Iffat Arefa; M. S. Alam; Ipshita Siddiquee; Nazmul Siddique

doi:10.1109/RAAICON48939.2019.8

Performance Analysis of Machine Learning Algorithms for Hypertension Decision Support System

Iffat Arefa, M. S. Alam, Ipshita Siddiquee, Nazmul Siddique

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

Machine learning algorithms are helpful to build a model-based decision support system using data to predict risk of hypertension disease which is deadly in Bangladesh as in other parts of the world. It is necessary to figure out which machine learning algorithm is suitable for implementing a decision support system practically. Therefore, in this work, 21 types of supervised machine learning algorithms have been employed training the prediction system for hypertension risk. Various types of Decision Trees, Logistic Regression, Support Vector Machines, Nearest Neighbors Classifiers and Ensemble Classifiers are used for training the model. 5 fold cross validation has been used in this case. 16 inputs are chosen based on expert knowledge and 2 outputs are selected as response. In this paper, performance is evaluated in terms of confusion matrix and ROC curve. 129 patients' data have been collected from local hospital to conduct this work.

Original language	English
Title of host publication	2019 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	15-20
Number of pages	6
ISBN (Electronic)	9781728158518
DOIs	https://doi.org/10.1109/RAAICON48939.2019.8
State	Published - Nov 2019
Externally published	Yes
Event	2019 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2019 - Dhaka, Bangladesh Duration: 29 Nov 2019 → 1 Dec 2019

Publication series

Name	2019 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2019

Conference

Conference	2019 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2019
Country/Territory	Bangladesh
City	Dhaka
Period	29/11/19 → 1/12/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Confusion Matrix
Hypertension (HTN)
Machine Learning
ROC curve

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Mechanical Engineering
Control and Optimization

Access to Document

10.1109/RAAICON48939.2019.8

Cite this

Arefa, I., Alam, M. S., Siddiquee, I., & Siddique, N. (2019). Performance Analysis of Machine Learning Algorithms for Hypertension Decision Support System. In 2019 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2019 (pp. 15-20). Article 9087522 (2019 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RAAICON48939.2019.8

Arefa, Iffat ; Alam, M. S. ; Siddiquee, Ipshita et al. / Performance Analysis of Machine Learning Algorithms for Hypertension Decision Support System. 2019 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 15-20 (2019 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2019).

@inproceedings{5fe694b52f954906a966a7c1c236759f,

title = "Performance Analysis of Machine Learning Algorithms for Hypertension Decision Support System",

abstract = "Machine learning algorithms are helpful to build a model-based decision support system using data to predict risk of hypertension disease which is deadly in Bangladesh as in other parts of the world. It is necessary to figure out which machine learning algorithm is suitable for implementing a decision support system practically. Therefore, in this work, 21 types of supervised machine learning algorithms have been employed training the prediction system for hypertension risk. Various types of Decision Trees, Logistic Regression, Support Vector Machines, Nearest Neighbors Classifiers and Ensemble Classifiers are used for training the model. 5 fold cross validation has been used in this case. 16 inputs are chosen based on expert knowledge and 2 outputs are selected as response. In this paper, performance is evaluated in terms of confusion matrix and ROC curve. 129 patients' data have been collected from local hospital to conduct this work.",

keywords = "Confusion Matrix, Hypertension (HTN), Machine Learning, ROC curve",

author = "Iffat Arefa and Alam, \{M. S.\} and Ipshita Siddiquee and Nazmul Siddique",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2019 ; Conference date: 29-11-2019 Through 01-12-2019",

year = "2019",

month = nov,

doi = "10.1109/RAAICON48939.2019.8",

language = "English",

series = "2019 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Arefa, I, Alam, MS, Siddiquee, I & Siddique, N 2019, Performance Analysis of Machine Learning Algorithms for Hypertension Decision Support System. in 2019 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2019., 9087522, 2019 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2019, Institute of Electrical and Electronics Engineers Inc., pp. 15-20, 2019 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2019, Dhaka, Bangladesh, 29/11/19. https://doi.org/10.1109/RAAICON48939.2019.8

Performance Analysis of Machine Learning Algorithms for Hypertension Decision Support System. / Arefa, Iffat; Alam, M. S.; Siddiquee, Ipshita et al.
2019 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 15-20 9087522 (2019 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Arefa, Iffat

AU - Alam, M. S.

AU - Siddiquee, Ipshita

AU - Siddique, Nazmul

PY - 2019/11

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N2 - Machine learning algorithms are helpful to build a model-based decision support system using data to predict risk of hypertension disease which is deadly in Bangladesh as in other parts of the world. It is necessary to figure out which machine learning algorithm is suitable for implementing a decision support system practically. Therefore, in this work, 21 types of supervised machine learning algorithms have been employed training the prediction system for hypertension risk. Various types of Decision Trees, Logistic Regression, Support Vector Machines, Nearest Neighbors Classifiers and Ensemble Classifiers are used for training the model. 5 fold cross validation has been used in this case. 16 inputs are chosen based on expert knowledge and 2 outputs are selected as response. In this paper, performance is evaluated in terms of confusion matrix and ROC curve. 129 patients' data have been collected from local hospital to conduct this work.

AB - Machine learning algorithms are helpful to build a model-based decision support system using data to predict risk of hypertension disease which is deadly in Bangladesh as in other parts of the world. It is necessary to figure out which machine learning algorithm is suitable for implementing a decision support system practically. Therefore, in this work, 21 types of supervised machine learning algorithms have been employed training the prediction system for hypertension risk. Various types of Decision Trees, Logistic Regression, Support Vector Machines, Nearest Neighbors Classifiers and Ensemble Classifiers are used for training the model. 5 fold cross validation has been used in this case. 16 inputs are chosen based on expert knowledge and 2 outputs are selected as response. In this paper, performance is evaluated in terms of confusion matrix and ROC curve. 129 patients' data have been collected from local hospital to conduct this work.

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M3 - Conference contribution

AN - SCOPUS:85085619998

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 29 November 2019 through 1 December 2019

ER -

Arefa I, Alam MS, Siddiquee I, Siddique N. Performance Analysis of Machine Learning Algorithms for Hypertension Decision Support System. In 2019 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 15-20. 9087522. (2019 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2019). doi: 10.1109/RAAICON48939.2019.8

Performance Analysis of Machine Learning Algorithms for Hypertension Decision Support System

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this