Review of: "FGSCare: A Feature-driven Grid Search-based Machine Learning Framework for Coronary Heart Disease Prediction"
Résumé
The abstract clearly states the problem (limitations of current ML in CHD prediction due to feature selection) and the proposed solution (FGSCare framework).It highlights the novelty in "systematically" ltering features and assessing the impact of feature selection on both traditional and deep learning models.This suggests a clear contribution.Introduction: The introduction expands on the limitations of traditional risk assessment models and the challenges in applying ML to CHD prediction.The stated objectives focus on a systematic comparison of ML/DL models and a speci c investigation of feature selection's impact.This systematic approach is a key novelty claim.Overall: The novelty seems to lie in:A structured framework (FGSCare) for feature selection in CHD prediction.A direct comparative analysis of feature selection's in uence across traditional ML and deep learning models speci cally for CHD.The combination of feature selection with SHAP-based interpretability.Potential Weakness: While the combination is novel, the individual techniques (feature selection, speci c ML models, SHAP) are well-established.The degree of novelty hinges on the strength of the Qeios qeios.com
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