Clustering Disease on Settlements Inhabitant In place seedy With Use Clustering Method
Why this work is in the frame
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Bibliographic record
Abstract
Residents living in slum areas often face serious problems related to public health, where the prevalence of disease tends to be high and its spread is difficult to control. The impact of the formation of slums for the community is that safety is threatened, health deteriorates, and social conditions worsen, causing many diseases for people living in slums. Therefore, this study aims to identify patterns and clusters of diseases that exist in residential areas in slums Binjai city using clustering method. The K-Means Algorithm clustering method was chosen because it is able to group data based on similar characteristics, so that it can help identify diseases in a more focused and efficient manner, using the MATLAB application is also very appropriate in this problem so that it can produce output from data mining that can be used in decision making. future decisions. By utilizing the data mining process using the clustering method, clustering can be a problem of grouping diseases in slum settlements. Based on the results of trials with 20 sample data conducted with MATLAB obtained in cluster 1 DHF cases with high slums, Cluster 2 cases of vomiting with moderate slums and cluster 3 cases of diarrhea with moderate slums. The results of this study are expected to provide in-depth insight into disease patterns and clusters in residential areas in slums.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it