RT Conference Proceedings
T1 Prediction of Metabolic Syndrome Based on Machine Learning Techniques with Emphasis on Feature Relevances and Explainability Analysis
A1 Ispizua, Begoña
A1 Manjarrés, Diana
A1 Niño-Adan, Iratxe
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
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.
PB Institute of Electrical and Electronics Engineers Inc.
SN 9798350337488
YR 2023
FD 2023
LK https://hdl.handle.net/11556/5008
UL https://hdl.handle.net/11556/5008
LA eng
NO 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
NO conference
NO Publisher Copyright: © 2023 IEEE.
DS TECNALIA Publications
RD 30 sept 2024