Feature Selection for Gestational Diabetes Mellitus Prediction using XAI based AutoML Approach

DOI

10.2991/978-94-6463-654-3_10How to use a DOI?

Keywords

IA; GDM; XAI; SHAP; AutoML

Abstract

Predicting Gestational Diabetes Mellitus (GDM) is crucial for pregnant women to enable regular monitoring of their blood sugar levels and adherence to a healthy diet. Early intervention can significantly lower the risk of developing this condition. To assess this risk, Machine Learning (ML) and Deep Learning techniques are employed. However, traditional ML models often face challenges in accurately predicting GDM risk due to the complex processing required to optimize their hyperparameters for the best performance. This study presents a feature selection for GDM prediction using AutoML-XAI techniques (Automatic Machine Learning – eXplainable Artificial Intelligence techniques) approach, which aims to automatically predict GDM risk as accurate as possible while providing meaningful interpretations of the predictive results used in feature selection. The AutoML models generated utilize a Kaggle dataset and several combinations of features selected based on their scores of importance determinated with XAI techniques such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations). The proposed approach of autoML and features selection with XAI techniques leads to the creation of a precise, efficient, and easily interpretable model which surpasses other machine learning models in predicting GDM risk without the need for human intervention. The scores of importance of features are involved in the feature selection process and multiple AutoML models are generated and assessed, with the optimal AutoML model being established automatically.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Alia Maaloul
AU  - Meriam Jemel
AU  - Nadia Ben Azzouna
PY  - 2025
DA  - 2025/02/24
TI  - Feature Selection for Gestational Diabetes Mellitus Prediction using XAI based AutoML Approach
BT  - Proceedings of the  International Conference on Decision Aid and Artificial Intelligence (ICODAI 2024)
PB  - Atlantis Press
SP  - 121
EP  - 135
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-654-3_10
DO  - 10.2991/978-94-6463-654-3_10
ID  - Maaloul2025
ER  -