Probability of Diabetes mellitus and Cardiometabolic Syndrome based on Facial Types
Bibliographic record
Abstract
With the advancement of technology it has been possible to detect diseases through non-invasive techniques. Previous studies showed us that diabetes mellitus and cardiometabolic syndrome are correlated disorders. Presence of one increased the possibility of existence of other. Efficient ways has been introduced to detect these disorders through facial image analysis using texture and color features. Furthermore, Human faces can be divided into three main categories based on the anatomical growth of their bones. Based on the knowledge the question arose that what could be the relation between facial types and these different categorical but linked disorders. Primary objective of this study is to analyze the facial types which have more probability of getting diabetes and cardiometabolic syndrome. Secondary objective includes the analysis of both genders i.e. male and female to detect which one of these genders are having more chances of these long lasting abnormalities. In the first step, data was acquired from four hospitals to make sure that it is accurate, both qualitatively and quantitatively. A few specifications were considered before capturing an image such as: frontal face, a distance of 3-4 feet between the camera and the subject, white background and neutral expressions. The age group of subjects was limited from of 30 to 80 years. The dataset consists of 198 participants including both male and female with equal number of subject in three groups namely diabetic, cardiometabolic and normal group. Facial index ratio was obtained after calculating the height and width of face using the anatomical landmarks described by researchers. After that each subject was classified into their respective group based on their facial ratio. Lastly, each group having different facial index ratio of project participant was analyzed statistically. Primary results showed that a large number of diabetic patients have wider and average faces also called mesofacial and brachifacial classes of face in medical terms. Further statistical analysis showed that there is a significant difference present between diabetic and normal group with P< 0.013 using CI of 95% and P<0.05. On the other hand, although large quantity of Cardiometabolic group belongs to mesofacial and brachifacial class but there was no concrete difference found between cardiometabolic and normal group using statistical analysis. Secondary results showed that in female there are more chances of these two disorders than male. The study concludes that rounder and average facial persons have more chances of diabetes mellitus and especially female are more affected by it. While there is no concrete result based showed that facial types have any relation with cardiometabolic syndrome.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".