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anjanaraj26/Survival-analysis-in-health-data

Statistical models and machine learning models on health care data

Survival Analysis in Health Data

A small, well-documented survival analysis project demonstrating both
classical biostatistics (Cox PH) and machine learning (Random Survival Forest)
for time-to-event outcomes in healthcare-style datasets.

What this project shows

  • Clean project structure (data / notebooks)
  • Survival modeling workflow (EDA → Modeling → Evaluation)
  • Ethical handling of health data (no raw patient-level data shared)

Methods

  • Kaplan–Meier exploration (EDA notebook)
  • Cox Proportional Hazards (interpretable hazard ratios)
  • Random Survival Forest (non-linear ML survival model)

Evaluation (planned / included as data allows)

  • Concordance Index (C-index)
  • Risk stratification and survival curves
  • Calibration (optional)

Repository structure

Survival-analysis-in-health-data/
├── data/        # Data description and access instructions (no raw data uploaded)
├── notebooks/   # 01_eda.ipynb, 02_modeling.ipynb (+ evaluation later)
├── README.md
└── .gitignore

Languages

Jupyter Notebook94.6%Python5.4%

Contributors

Created January 5, 2026
Updated January 9, 2026
anjanaraj26/Survival-analysis-in-health-data | GitHunt