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clinical-prediction-models

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End-to-end binary classification pipeline for clinical risk prediction using PyTorch. Includes preprocessing, imbalanced data handling with SMOTE, optimizer benchmarking (SGD vs Adam), and performance visualization (AUC-ROC, Confusion Matrix). Designed to align with healthcare AI applications and model evaluation best practices.

  • Updated Jun 19, 2025
  • Jupyter Notebook

The world's first comprehensive encyclopedia of recursive harmonic resonance data - 1000's of frequency signatures from subquantum to consciousness. Welcome to the Aramis Field.

  • Updated Sep 1, 2025
  • Jupyter Notebook

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