The application of multiple linear regression methods to FTIR spectra of fingernails for predicting gender and age of human subjects
Bibliographic record
Abstract
The paper explores the accuracy of gender and age prediction of human subjects based on the chemometric analysis of FTIR spectra from fingernails. The baseline and scaling over the 0-1 range were applied to FTIR spectra from fingernails of 123 subjects, and wavenumbers for which absorbance values showed a statistically significant correlation with gender and age were identified. The prediction accuracy was analyzed using: Multiple linear regression, Forward stepwise regression, Backward stepwise regression, Principal component regression, and Partial least squares regression. As regard the gender prediction, the principal component regression model proved to be the most accurate, with 8 extracted components allowing a prediction of 93.50 % (86.00 % for women, 98.63 % for men). The predictive power of the model showed that in case of new subjects, gender classification could be done in 91.06 % of cases (84.00 % for women, 95.89 % for men). For age prediction, the optimal backward stepwise regression model with 6 statistically significant predictors showed an average error of 11.39 % (13.70 % for women, 9.81 % for men). The predictive power of the model was 12.13 % (11.73 % for women, 10.35 % for men). The age prediction was less accurate, with a maximum error of 10.00 % achieved only in the case of 65.04 % of subjects.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".