Prediction of Hepatitis C based on liver function test features
Why this work is in the frame
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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. χ<sup>2</sup> 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
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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