Classification Of Diseases In Patients Based On Factors Environment Using The K-Means Algorithm At Puskesmas Subdistrict Selesai
Bibliographic record
Abstract
Diseases caused by the environment are disease phenomena caused by the relationship between humans and environmental factors. Diseases that occur due to the environment that must be known by the public are such as ISPA, dermatitis, diarrhea, pulmonary TB, and so on. In the area of the Kecamatan Selesai, there are still many environmental conditions Not yet such as damaged roads and smoke from factories that cause air pollution, so with condition environment like This can affect public health. Puskesmas Selesai is Public health center Which located in region Kecamatan Selesai. The data of patients seeking treatment at this puskesmas are only used archives and to view the patient's medical history. The public should know about symptoms of the disease in order to get appropriate services. In data mining techniques for clustering patient disease data can be used as new information useful for puskesmas or related as material counseling to society. The purpose of this study is to analyze the results of the application of data mining using K-Means Clustering in grouping patient diseases based on the environment with age, village and disease diagnoses variables.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".