Answer:
- Identify duplicates by comparing unique identifiers (e.g., student ID or a combination of name and age).
- Remove exact duplicates or consolidate partial duplicates by merging relevant information. For instance, if two records show the same student but different treatment dates, combine them into one record with both treatments noted.
Citation: Rahm, E., & Do, H. H. (2000). Data cleaning: Problems and current approaches. IEEE Data Engineering Bulletin, 23(4), 3-13.
If it is for research:
Remove
If it is for research with the intention to create synthetic cases: change identifiers with similar
If it is for clinical records history:
Compare and remove oldest, except missing data in newest record. Only cross over what is relevant and can be veriffied.
If actual clinicalncase:
Rverify data elemetn for databelement and only add to the newest cq target record.
If it is a purely a duplicate record, you can select only unique records during your dataset preparation.
If it is a case of disjointed records, you need to think about what you need and whether you are able to merge these records. If there are a few such records and dropping them is inconsequential or you are not time bound, you can drop the records.
Handling duplicate records is crucial for maintaining data integrity. Here are some steps you can take:
Anitha Roshan Sagarkar