2024

Conférence

CoDIT 2024: 1939-1944

Gestational Diabetes Mellitus (GDM) is a type of diabetes that develops during pregnancy. It is important for pregnant women to monitor their blood sugar levels regularly and follow a healthy diet. However, early intervention can greatly reduce risk of this type of diabetes. Machine Learning and Deep Learning techniques are utilized to predict this risk based on an individual's symptoms, lifestyle, and medical history. By identifying key features such as age, insulin, body mass index, and glucose levels, machine learning models such as Random Forest and XGBoost are used in this research work to classify patients at risk of a gestational diabetes. In addition, we propose an explainable feature selection approach to improve the accuracy of machine learning models for GDM prediction. This method involves iteratively eliminating features that exhibit a negative contribution as determined by the SHAP (Shapley Additive explanations) feature attribution explanations for the model’s predictions.

@inproceedings{inproceedings,
author = {Maaloul, Alia and Jemel, Meriam and Ben Azzouna, Nadia},
year = {2024},
month = {07},
pages = {1939-1944},
title = {XAI based feature selection for gestational diabetes Mellitus prediction},
doi = {10.1109/CoDIT62066.2024.10708408}
}