Enact Business Solutions2025Private
Coronary Angiogram Dataset Cleaning
Rule-driven cleaning for coronary angiogram records and derived clinical features.
- Role
- Data Engineer
- Work track
- Enact Business Solutions
- Category
- Data Engineering
Context and problem
Coronary angiogram records required normalization before reliable downstream grouping and analysis.
Execution and contribution
Applied clinical cleaning rules, severity normalization, derived feature generation, and validation checks.
Outcomes and proof
Recorded results and supplied evidence from the work.
- 01
- Clinical rules
- 02
- Severity normalization
- 03
- Derived features
- Cleaned coronary angiogram fields.
- Standardized severity and derived indicators.
- Improved dataset readiness for analytics.
- Domain-informed data quality engineering for specialized clinical datasets.
Available evidence
- Rule-based cleaning workflow
- Feature engineering quality validation notes
Gallery and evidence
Healthcare Data Flow and supplied project material.
Technical record
Stack
- Python
- Pandas
- Clinical Rules
- Data Validation
Focus
- Coronary
- Angiogram
- Clinical Analytics
Confidentiality
Private healthcare workflow. Sensitive records are excluded.Related work
More records from the same work track or technical category.
