Optimized Supervised Learning Framework for Gestational Diabetes Mellitus (GDM) Detection based on Recursive Feature Elimination (RFE)
DOI:
https://doi.org/10.11113/mjfas.v21n6.4462Keywords:
Gestational Diabetes Mellitus, Recursive Feature Elimination, Feature Selection, Random Forest.Abstract
Supervised machine learning has been widely applied in healthcare, yet few studies emphasize feature selection's role in enhancing predictive accuracy. Identifying research that exclusively employs feature selection for GDM detection remains challenging. This study designs an optimized supervised learning framework using Recursive Feature Elimination (RFE) to improve early GDM detection and assist medical professionals in patient evaluation. RFE systematically ranks features by iteratively removing the least relevant ones until no further improvement through various estimators. The selected features are then used to build several frameworks based on five well established machine learning models. Results indicate that the integration of Recursive Feature Elimination (RFE) with a Random Forest as both estimator and classifier achieves the highest accuracy (97.31%) and F1-score (96.6%). These findings demonstrate RFE’s effectiveness in optimizing feature selection, enhancing model accuracy, and reducing computational cost through utilizing 6 number of features. In conclusion, incorporating RFE within supervised learning frameworks significantly improves GDM detection. This research contributes to developing automated diagnostic tools that assist healthcare professionals in evaluating patient health and predicting diseases, ultimately enhancing early diagnosis and improving patient outcomes, especially for pregnant women at risk of GDM.
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