Diagnosa Tingkat Kolesterol Pada Remaja Menggunakan Metode Dempster Shafer
Bibliographic record
Abstract
Teenagers are the age group from 10 years to before the age of 23 years. Adolescent health efforts aim to prepare adolescents to become healthy, intelligent, qualified and productive adults who play a role in maintaining, maintaining and improving their own health. In its grouping, cholesterol is included in the steroid group, which is a type of lipid that is not connected and is a fatty substance that has the property of not dissolving in the blood, which in the transportation process requires the help of protein to form particles which are usually called lipoproteins. Lipoprotein itself has several types, including LDL (low density lipoprotein), HDL (high density lipoprotein), triglycerides and total cholesterol. Dempster Shafer is the Dempster Shafer Method, also known as belief function theory. This method uses Belief, which is a measure of the strength of evidence in supporting a set of propositions. If the value is 0 (zero), it indicates that there is no evidence, and if the value is 1, it indicates that there is certainty. Based on the weight values given by experts for each data on symptoms of cholesterol disease in adolescents, from the results of the analysis carried out in the previous chapter, the results of the diagnosis of cholesterol disease in adolescents were obtained, namely Low Density Lipoprotein (LDL) disease with a density value of 73.81%.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".