Data Mining Analysis For Medical Record Data Clasterization Using K-Means Algorithm In Sylvani Binjai Hospital
Bibliographic record
Abstract
Medical record is a record of the history of patients who take treatment in hospitals or clinics. RSU Sylvani has many patients, every month and makes patient history data accumulate in the medical record data, but there is no follow-up benefit from the available data. Even though these data have great potential to provide new information and valuable insights if explored with data mining using the k-means clustering method. The amount of data that was tested was 893 data and produced 4 groups from the variables of disease diagnosis, gender and address. Where group 1 totaled 268 data with a diagnosis center for hypertension and female gender at the Pepper Garden address. Group 2 totaled 289 data with a diagnosis center for Asthma and female gender at the Hero's address. Group 3 totaled 185 data with GERD disease diagnosis center and male gender at Kebun Pepper address. group 4 totaling 151 data with a diagnosis center for Prostate Enlargement disease and male sex at Kebun Pepper address.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".