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M.Sc. Thesis: Survival Analysis on Orthopedic Disease Data
2017–2019Survival Models
Context: Primary field data collection on real orthopedic disease patients.
Objective: Identify the optimal predictive model for disease recurrence.
Methodology: Applied Kaplan-Meier curves, Log-Rank tests, hazard modeling, and Random Survival Forests across two successive research projects.
Models Evaluated: Cox PH, XGBoost, CoxBoost, GLMBoost, GLM, CForest.
Outcome: Comparative evaluation of 6 survival models to determine the best predictor for disease recurrence, with findings documented in a full research thesis.
[ Diagram Placeholder: Kaplan-Meier Curves / Model Comparison Chart ]
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Statistical Tools Used:
RSurvival AnalysisKaplan-MeierCox PHRandom Survival ForestXGBoost