Using correspondence analysis and log-linear models to investigate the factors affecting cardiovascular disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cardiovascular disease is the main cause of mortality in the World. This issue has seriously alarmed governments of developed and developing countries both. Diseases related to the heart play a role as the highest risk for human health. There are many factors contributing to the development of these diseases including poor diet, sedentary lifestyle, high blood pressure and hypertension. In this paper, we present a study of the influence of different factors by the correspondence analysis and log-linear models to deal with prediction of cardiovascular disease development. A survey has been conducted amongst affected people of different age groups, gen-der, and various education levels. Based on this data, we could determine which group would beat the higher risk leading to the cardiovascular disease. It should be noted that all participants were suffering from cardiovascular disease either slightly or seriously. Our findings show that women are at higher risk than men being affected by cardiovascular disease. Moreover, different factors such as smoking, high cholesterol level, physical inactivity and poor diet contribute significantly to the possibility for this disease. Via our analyses, we also can obtain a better comprehension of the data structure and better interpretation of the results by combining two approach-es (correspondence analysis and log-linear models). Also, it is concluded that correspondence analysis allows us to find the strong correlations between involving variables. That could lead to the conception of prognostic and biomechanical models using the inter-correlations between variables and building a good structure of big data in the future.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it