Prediction of Hepatitis C based on liver function test features
Bibliographic record
Abstract
Hepatitis C is a widespread liver disease that possibly leads to serious symptoms if not diagnosed in time. Currently, several methods are already available for specific screening of Hepatitis C. However, their expensive costs make it hard to allow their broad use in countries with poor conditions. Here, by constructing a mathematical model, we introduce a new method for testing Hepatitis C diagnosis. Our method is based on the results of liver function tests; therefore, it is relatively more cost-saving to do the test. A study was conducted based on the dataset obtained from the UCI Machine Learning Repository at June 10, 2020, containing laboratory values of blood donors and Hepatitis C patients and demographic values like age. χ2 and ANOVA test was used to find the correlation between Hepatitis C and parameters of liver function test. Logistics regression was used to build the model for the prediction of Hepatitis C. The result shows that there’s a significant increase in likelihood of Hepatitis C when there’s increase in AST (β = 0.09, p < 0.001) and BIL (β = 0.057, p < 0.01); and there’s also a significant decrease in likelihood of Hepatitis C when there’s increase in ALT (β = -0.026, p < 0.001) and CHOL (β
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".