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dataimbalance

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Karthika-Ramalingam / Reducing-False-Negatives-in-Heart-Disease-Prediction-Using-SMOTE

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Machine learning project for predicting Coronary Artery Disease (CAD) using Logistic Regression and Random Forest. Compares two imbalance handling strategies, Class Weighting and SMOTE, on a highly imbalanced medical dataset with GridSearchCV-based hyperparameter tuning and performance evaluation.

python data-science machine-learning random-forest machine-learning-algorithms prediction classification logistic-regression machinelearning confusion-matrix smote oversampling heart-disease imblearn binaryclassification randomforestclassifier smote-sampling smote-oversampler dataimbalance
  • Updated Jul 27, 2026
  • Jupyter Notebook

adilrasheed139 / Fairness-Auditing-and-Mitigation-in-Cardiac-MRI-Segmentation-From-Data-Imbalance-to-Vendor-Bias

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This study audits and mitigates fairness issues in cardiac MRI segmentation across SIEMENS, Philips, and GE scanners. A baseline 2D U-Net showed spurious vendor bias, particularly for the minority GE domain. Implementing a Domain Adversarial Neural Network reduced F1-Score disparity, stabilizing recall and improving clinical safety.

auditing pytorch mri segmentation deeplearning equity bias fairness mitigation u-net medicalai dann medicalimaging domain-adversarial-neural-networks healthcareai cardiacmri dataimbalance vendorbias clinicalai
  • Updated Dec 20, 2025
  • Jupyter Notebook

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