Application of the K-Nearest Neighbor Method for Classification of Hypertension Diseases (Case Study: Stabat Health Center)
Bibliographic record
Abstract
Globally, the WHO (World Health Organization) estimates that non-communicable diseases cause about 60% of deaths and 43% of diseases worldwide. Hypertension is a disease that occurs due to an increase in blood pressure in humans. It is difficult to know if a person has hypertension, without measuring the patient's blood pressure. According to the American Heart Association (AHA), the number of Americans over the age of 20 suffering from hypertension has reached 74.5 million, but nearly 90-95% of cases have no known cause. It is estimated that about 80% of the increase in hypertension cases will occur mainly in developing countries by 2025, from 639 million cases in 2000. This number is expected to increase to 1.15 billion cases in 2023. This study uses a quantitative approach with experimental methods to test the application of K-Nearest Neighbor (KNN) in the classification of hypertension diseases at the Stabat Health Center. The description of the results obtained is to make the right decision regarding when and how to treat the disease to prevent the worst possibility for patients by classifying the severity of hypertension both in normal circumstances, prehypertension, stage 1 hypertension, and stage 2 hypertension. The results of the trial show that the KNN model is able to provide accurate predictions based on patient history data available at Stabat Health Center.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".