Prediction of Metabolic Syndrome Based on Machine Learning Techniques with Emphasis on Feature Relevances and Explainability Analysis

Begoña Ispizua; Diana Manjarrés; Iratxe Niño-Adan

doi:10.1109/BIBM58861.2023.10385780

Prediction of Metabolic Syndrome Based on Machine Learning Techniques with Emphasis on Feature Relevances and Explainability Analysis

Begoña Ispizua^*
, Diana Manjarrés
, Iratxe Niño-Adan

^*Corresponding author for this work

AI

Fundación Sarenet

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

2 Citations (Scopus)

Abstract

Metabolic syndrome (MetS) is considered to be a major public health problem worldwide leading to a high risk of diabetes and cardiovascular diseases. In this paper, data collected by the Precision Medicine Initiative of the Basque Country, named the AKRIBEA project, is employed to infer via Machine Learning (ML) techniques the features that have the most influence on predicting MetS in the general case and also separately by gender. Different Feature Normalization (FN) and Feature Weighting (FW) methods are applied and an exhaustive analysis of explainability by means of Shapley Additive Explanations (SHAP) and feature relevance methods is performed. Validation results show that the Extreme Gradient Boosting (XGB) with Min-Max FN and Mutual Information FW achieves the best trade-off between precision and recall performance metrics.

Original language	English
Title of host publication	Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Editors	Xingpeng Jiang, Haiying Wang, Reda Alhajj, Xiaohua Hu, Felix Engel, Mufti Mahmud, Nadia Pisanti, Xuefeng Cui, Hong Song
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1989-1992
Number of pages	4
ISBN (Electronic)	9798350337488
DOIs	https://doi.org/10.1109/BIBM58861.2023.10385780
Publication status	Published - 2023
Event	2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 - Istanbul, Turkey Duration: 5 Dec 2023 → 8 Dec 2023

Publication series

Name	Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Conference

Conference	2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Country/Territory	Turkey
City	Istanbul
Period	5/12/23 → 8/12/23

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

classification
explainability
feature relevances
machine learning
metabolic syndrome

Access to Document

10.1109/BIBM58861.2023.10385780

Cite this

Ispizua, B., Manjarrés, D., & Niño-Adan, I. (2023). Prediction of Metabolic Syndrome Based on Machine Learning Techniques with Emphasis on Feature Relevances and Explainability Analysis. In X. Jiang, H. Wang, R. Alhajj, X. Hu, F. Engel, M. Mahmud, N. Pisanti, X. Cui, & H. Song (Eds.), Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 (pp. 1989-1992). (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM58861.2023.10385780

Ispizua, Begoña ; Manjarrés, Diana ; Niño-Adan, Iratxe. / Prediction of Metabolic Syndrome Based on Machine Learning Techniques with Emphasis on Feature Relevances and Explainability Analysis. Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. editor / Xingpeng Jiang ; Haiying Wang ; Reda Alhajj ; Xiaohua Hu ; Felix Engel ; Mufti Mahmud ; Nadia Pisanti ; Xuefeng Cui ; Hong Song. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1989-1992 (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023).

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title = "Prediction of Metabolic Syndrome Based on Machine Learning Techniques with Emphasis on Feature Relevances and Explainability Analysis",

abstract = "Metabolic syndrome (MetS) is considered to be a major public health problem worldwide leading to a high risk of diabetes and cardiovascular diseases. In this paper, data collected by the Precision Medicine Initiative of the Basque Country, named the AKRIBEA project, is employed to infer via Machine Learning (ML) techniques the features that have the most influence on predicting MetS in the general case and also separately by gender. Different Feature Normalization (FN) and Feature Weighting (FW) methods are applied and an exhaustive analysis of explainability by means of Shapley Additive Explanations (SHAP) and feature relevance methods is performed. Validation results show that the Extreme Gradient Boosting (XGB) with Min-Max FN and Mutual Information FW achieves the best trade-off between precision and recall performance metrics.",

keywords = "classification, explainability, feature relevances, machine learning, metabolic syndrome",

author = "Bego{\~n}a Ispizua and Diana Manjarr{\'e}s and Iratxe Ni{\~n}o-Adan",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 ; Conference date: 05-12-2023 Through 08-12-2023",

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doi = "10.1109/BIBM58861.2023.10385780",

language = "English",

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editor = "Xingpeng Jiang and Haiying Wang and Reda Alhajj and Xiaohua Hu and Felix Engel and Mufti Mahmud and Nadia Pisanti and Xuefeng Cui and Hong Song",

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Ispizua, B , Manjarrés, D & Niño-Adan, I 2023, Prediction of Metabolic Syndrome Based on Machine Learning Techniques with Emphasis on Feature Relevances and Explainability Analysis. in X Jiang, H Wang, R Alhajj, X Hu, F Engel, M Mahmud, N Pisanti, X Cui & H Song (eds), Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, Institute of Electrical and Electronics Engineers Inc., pp. 1989-1992, 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, Istanbul, Turkey, 5/12/23. https://doi.org/10.1109/BIBM58861.2023.10385780

Prediction of Metabolic Syndrome Based on Machine Learning Techniques with Emphasis on Feature Relevances and Explainability Analysis. / Ispizua, Begoña ; Manjarrés, Diana; Niño-Adan, Iratxe.
Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. ed. / Xingpeng Jiang; Haiying Wang; Reda Alhajj; Xiaohua Hu; Felix Engel; Mufti Mahmud; Nadia Pisanti; Xuefeng Cui; Hong Song. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1989-1992 (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023).

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

TY - GEN

T1 - Prediction of Metabolic Syndrome Based on Machine Learning Techniques with Emphasis on Feature Relevances and Explainability Analysis

AU - Ispizua, Begoña

AU - Manjarrés, Diana

AU - Niño-Adan, Iratxe

PY - 2023

Y1 - 2023

N2 - Metabolic syndrome (MetS) is considered to be a major public health problem worldwide leading to a high risk of diabetes and cardiovascular diseases. In this paper, data collected by the Precision Medicine Initiative of the Basque Country, named the AKRIBEA project, is employed to infer via Machine Learning (ML) techniques the features that have the most influence on predicting MetS in the general case and also separately by gender. Different Feature Normalization (FN) and Feature Weighting (FW) methods are applied and an exhaustive analysis of explainability by means of Shapley Additive Explanations (SHAP) and feature relevance methods is performed. Validation results show that the Extreme Gradient Boosting (XGB) with Min-Max FN and Mutual Information FW achieves the best trade-off between precision and recall performance metrics.

AB - Metabolic syndrome (MetS) is considered to be a major public health problem worldwide leading to a high risk of diabetes and cardiovascular diseases. In this paper, data collected by the Precision Medicine Initiative of the Basque Country, named the AKRIBEA project, is employed to infer via Machine Learning (ML) techniques the features that have the most influence on predicting MetS in the general case and also separately by gender. Different Feature Normalization (FN) and Feature Weighting (FW) methods are applied and an exhaustive analysis of explainability by means of Shapley Additive Explanations (SHAP) and feature relevance methods is performed. Validation results show that the Extreme Gradient Boosting (XGB) with Min-Max FN and Mutual Information FW achieves the best trade-off between precision and recall performance metrics.

KW - classification

KW - explainability

KW - feature relevances

KW - machine learning

KW - metabolic syndrome

UR - https://www.scopus.com/pages/publications/85184906046

U2 - 10.1109/BIBM58861.2023.10385780

DO - 10.1109/BIBM58861.2023.10385780

M3 - Conference contribution

AN - SCOPUS:85184906046

T3 - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

SP - 1989

EP - 1992

BT - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

A2 - Jiang, Xingpeng

A2 - Wang, Haiying

A2 - Alhajj, Reda

A2 - Hu, Xiaohua

A2 - Engel, Felix

A2 - Mahmud, Mufti

A2 - Pisanti, Nadia

A2 - Cui, Xuefeng

A2 - Song, Hong

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Y2 - 5 December 2023 through 8 December 2023

ER -

Ispizua B , Manjarrés D, Niño-Adan I. Prediction of Metabolic Syndrome Based on Machine Learning Techniques with Emphasis on Feature Relevances and Explainability Analysis. In Jiang X, Wang H, Alhajj R, Hu X, Engel F, Mahmud M, Pisanti N, Cui X, Song H, editors, Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1989-1992. (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023). doi: 10.1109/BIBM58861.2023.10385780

Prediction of Metabolic Syndrome Based on Machine Learning Techniques with Emphasis on Feature Relevances and Explainability Analysis

Abstract

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UN SDGs

Keywords

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