Heart Disease: Application of the K-Nearest Neighbor (KNN) Method
Bibliographic record
Abstract
The increasing prevalence of non-communicable diseases, especially heart disease, in Indonesia highlights the need to improve the effectiveness of medical procedures, especially for patients with heart disease.Data and information from the Ministry of Health of the Republic of Indonesia show that non-communicable diseases, especially heart disease, are the most prevalent diseases and account for 16% of all cases worldwide.The purpose of this study is to analyze the factors associated with the prevalence of heart disease in Indonesia, based on the frequency of gender, age, slope, blood sugar, and chest pain.This study uses the K-Nearest Neighbor algorithm method in data mining with several stages.Dataset, Preprocessing, and Clustering are stages that must be done in this research, this system is to capture patient information to be implemented.This challenge is applied by exploring some initial approaches to obtain the value of K.This is especially true for very large data.The results of this study can contribute to the understanding of data miningbased cardiac data analysis techniques using the K-Nearest Neighbor algorithm to improve the accuracy of diagnosing cardiac patients, with special attention to several types of frequencies that are key in this method.This study also obtained the results of the classification of heart disease datasets with 72.13% based on the maximum K value of KNN through the K-Nearest Neighbor clustering technique.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".