Optimization of an Analysis Method for Diabetes Prediction Using Classical and Ensemble Machine Learning Techniques

Edison Naranjo; Berenice Arguero; Remigio Hurtado

doi:10.1007/978-981-97-3559-4_43

Optimization of an Analysis Method for Diabetes Prediction Using Classical and Ensemble Machine Learning Techniques

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

Abstract

Nowadays, diabetes has become a prevalent and significant illness worldwide, causing harm to the circulatory system and leading to complications such as vision loss, kidney problems, and heart disorders. Detecting diabetes early on is crucial in order to implement more effective treatments, control blood sugar levels, and reduce the risk of associated complications affecting both small and large blood vessels. It also provides an opportunity to make lifestyle changes and use targeted medications before irreversible damage occurs in organs and tissues. To achieve this, a method based on CRISP-DM is proposed, which utilizes five traditional machine learning algorithms and ensemble techniques, including RandomForest, DecisionTree, XGboost, Logistic Regression, and Neural Networks. These algorithms are applied to a dataset containing 15,000 records from the National Institute of Diabetes and Digestive and Kidney Diseases [1]. To assess the effectiveness of the predictive models, quality measures such as Accuracy, Precision, Recall, and F1-Score are used for comparison.

Original language	English
Title of host publication	Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024
Editors	Xin-She Yang, Simon Sherratt, Nilanjan Dey, Amit Joshi
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	527-536
Number of pages	10
ISBN (Print)	9789819735587
DOIs	https://doi.org/10.1007/978-981-97-3559-4_43
State	Published - 2024
Event	9th International Congress on Information and Communication Technology, ICICT 2024 - London, United Kingdom Duration: 19 Feb 2024 → 22 Feb 2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1013 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	9th International Congress on Information and Communication Technology, ICICT 2024
Country/Territory	United Kingdom
City	London
Period	19/02/24 → 22/02/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Cross-validation
Decision tree
Diabetes prediction
Logistic regression
Machine learning
Neural network
Random forest
XGBoost

CACES Knowledge Areas

8116A Information Systems
116A Computer Science

Access to Document

10.1007/978-981-97-3559-4_43

Cite this

Naranjo, E., Arguero, B., & Hurtado, R. (2024). Optimization of an Analysis Method for Diabetes Prediction Using Classical and Ensemble Machine Learning Techniques. In X.-S. Yang, S. Sherratt, N. Dey, & A. Joshi (Eds.), Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024 (pp. 527-536). (Lecture Notes in Networks and Systems; Vol. 1013 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-97-3559-4_43

Naranjo, Edison ; Arguero, Berenice ; Hurtado, Remigio. / Optimization of an Analysis Method for Diabetes Prediction Using Classical and Ensemble Machine Learning Techniques. Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024. editor / Xin-She Yang ; Simon Sherratt ; Nilanjan Dey ; Amit Joshi. Springer Science and Business Media Deutschland GmbH, 2024. pp. 527-536 (Lecture Notes in Networks and Systems).

@inproceedings{c785d8ab289642a4866cc1d5ccb3b4a6,

title = "Optimization of an Analysis Method for Diabetes Prediction Using Classical and Ensemble Machine Learning Techniques",

abstract = "Nowadays, diabetes has become a prevalent and significant illness worldwide, causing harm to the circulatory system and leading to complications such as vision loss, kidney problems, and heart disorders. Detecting diabetes early on is crucial in order to implement more effective treatments, control blood sugar levels, and reduce the risk of associated complications affecting both small and large blood vessels. It also provides an opportunity to make lifestyle changes and use targeted medications before irreversible damage occurs in organs and tissues. To achieve this, a method based on CRISP-DM is proposed, which utilizes five traditional machine learning algorithms and ensemble techniques, including RandomForest, DecisionTree, XGboost, Logistic Regression, and Neural Networks. These algorithms are applied to a dataset containing 15,000 records from the National Institute of Diabetes and Digestive and Kidney Diseases [1]. To assess the effectiveness of the predictive models, quality measures such as Accuracy, Precision, Recall, and F1-Score are used for comparison.",

keywords = "Cross-validation, Decision tree, Diabetes prediction, Logistic regression, Machine learning, Neural network, Random forest, XGBoost",

author = "Edison Naranjo and Berenice Arguero and Remigio Hurtado",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 9th International Congress on Information and Communication Technology, ICICT 2024 ; Conference date: 19-02-2024 Through 22-02-2024",

year = "2024",

doi = "10.1007/978-981-97-3559-4\_43",

language = "English",

isbn = "9789819735587",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "527--536",

editor = "Xin-She Yang and Simon Sherratt and Nilanjan Dey and Amit Joshi",

booktitle = "Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024",

address = "Germany",

}

Naranjo, E, Arguero, B & Hurtado, R 2024, Optimization of an Analysis Method for Diabetes Prediction Using Classical and Ensemble Machine Learning Techniques. in X-S Yang, S Sherratt, N Dey & A Joshi (eds), Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024. Lecture Notes in Networks and Systems, vol. 1013 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 527-536, 9th International Congress on Information and Communication Technology, ICICT 2024, London, United Kingdom, 19/02/24. https://doi.org/10.1007/978-981-97-3559-4_43

Optimization of an Analysis Method for Diabetes Prediction Using Classical and Ensemble Machine Learning Techniques. / Naranjo, Edison; Arguero, Berenice ; Hurtado, Remigio.
Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024. ed. / Xin-She Yang; Simon Sherratt; Nilanjan Dey; Amit Joshi. Springer Science and Business Media Deutschland GmbH, 2024. p. 527-536 (Lecture Notes in Networks and Systems; Vol. 1013 LNNS).

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

TY - GEN

T1 - Optimization of an Analysis Method for Diabetes Prediction Using Classical and Ensemble Machine Learning Techniques

AU - Naranjo, Edison

AU - Arguero, Berenice

AU - Hurtado, Remigio

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

PY - 2024

Y1 - 2024

N2 - Nowadays, diabetes has become a prevalent and significant illness worldwide, causing harm to the circulatory system and leading to complications such as vision loss, kidney problems, and heart disorders. Detecting diabetes early on is crucial in order to implement more effective treatments, control blood sugar levels, and reduce the risk of associated complications affecting both small and large blood vessels. It also provides an opportunity to make lifestyle changes and use targeted medications before irreversible damage occurs in organs and tissues. To achieve this, a method based on CRISP-DM is proposed, which utilizes five traditional machine learning algorithms and ensemble techniques, including RandomForest, DecisionTree, XGboost, Logistic Regression, and Neural Networks. These algorithms are applied to a dataset containing 15,000 records from the National Institute of Diabetes and Digestive and Kidney Diseases [1]. To assess the effectiveness of the predictive models, quality measures such as Accuracy, Precision, Recall, and F1-Score are used for comparison.

AB - Nowadays, diabetes has become a prevalent and significant illness worldwide, causing harm to the circulatory system and leading to complications such as vision loss, kidney problems, and heart disorders. Detecting diabetes early on is crucial in order to implement more effective treatments, control blood sugar levels, and reduce the risk of associated complications affecting both small and large blood vessels. It also provides an opportunity to make lifestyle changes and use targeted medications before irreversible damage occurs in organs and tissues. To achieve this, a method based on CRISP-DM is proposed, which utilizes five traditional machine learning algorithms and ensemble techniques, including RandomForest, DecisionTree, XGboost, Logistic Regression, and Neural Networks. These algorithms are applied to a dataset containing 15,000 records from the National Institute of Diabetes and Digestive and Kidney Diseases [1]. To assess the effectiveness of the predictive models, quality measures such as Accuracy, Precision, Recall, and F1-Score are used for comparison.

KW - Cross-validation

KW - Decision tree

KW - Diabetes prediction

KW - Logistic regression

KW - Machine learning

KW - Neural network

KW - Random forest

KW - XGBoost

UR - https://www.scopus.com/pages/publications/85201015966

U2 - 10.1007/978-981-97-3559-4_43

DO - 10.1007/978-981-97-3559-4_43

M3 - Conference contribution

AN - SCOPUS:85201015966

SN - 9789819735587

T3 - Lecture Notes in Networks and Systems

SP - 527

EP - 536

BT - Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024

A2 - Yang, Xin-She

A2 - Sherratt, Simon

A2 - Dey, Nilanjan

A2 - Joshi, Amit

PB - Springer Science and Business Media Deutschland GmbH

T2 - 9th International Congress on Information and Communication Technology, ICICT 2024

Y2 - 19 February 2024 through 22 February 2024

ER -

Naranjo E, Arguero B , Hurtado R. Optimization of an Analysis Method for Diabetes Prediction Using Classical and Ensemble Machine Learning Techniques. In Yang XS, Sherratt S, Dey N, Joshi A, editors, Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024. Springer Science and Business Media Deutschland GmbH. 2024. p. 527-536. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-97-3559-4_43