Analysis of Genetic Face Images with Respect to Reflexology for Prediction of Diseases
Bibliographic record
Abstract
A genetic disease or disorders is a hereditary issue caused by one or more abnormalities formed in the genome.Genetic disorders can be monogenic, multifactorial, or chromosomal.Like genetic disorders, facial features are also passed down genetically.This paper proposes to identify genetic disorders from facial features.However, it does not explain which facial features led to its prediction.In order to overcome the issues, face reflexology regions are analysed to predict the genetic diseases.Face reflexology regions are related to the internal organs and structure of the body.Genetic faces are analyzed with respect to face reflexology regions for the prediction of genetic diseases.Feature vectors are generated for the reflexology regions using Local binary pattern (LBP) with the combination of high frequency and low frequency textures.The Euclidean distance weight function is used for prediction of diseases using the feature vectors.The proposed method is not only using single face reflexology regions, but combined reflexology regions of n persons are used for finding multiple possibility of diseases.Based on the statistical measure analysis, the proposed algorithm works well in extracting the features for identifying the diseases linked to genetic disorders, potentially speeding up diagnosis of diseases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".