University / Personal Experiments2023Research
Breast Cancer Survival Prediction
SEER survival analysis with Kaplan-Meier, Cox PH, and Random Survival Forest models.
- Role
- Research Engineer
- Work track
- University / Personal Experiments
- Category
- Clinical ML / Predictive Analytics
Context and problem
Population-level breast cancer outcomes needed interpretable survival modeling for risk-aware analysis.
Execution and contribution
Built survival analysis workflows comparing statistical and machine learning methods over clinical feature distributions.
Outcomes and proof
Recorded results and supplied evidence from the work.
- 01
- ICECET 2023
- 02
- SEER dataset
- 03
- Survival models
- Published related survival-analysis work at ICECET 2023.
- Compared interpretable survival methods.
- Connected clinical ML to research evidence.
- Clinical ML pipeline design with statistical rigor and evaluation-focused decision support framing.
Available evidence
- Clinical ML pipeline structure
- Survival analysis evaluation figure
Gallery and evidence
Clinical ML Pipeline and supplied project material.
Technical record
Stack
- Python
- Scikit-learn
- Survival Analysis
- SEER
Focus
- Clinical ML
- Healthcare
- Survival Analysis
Resources
Related work
More records from the same work track or technical category.
