Using correspondence analysis and log-linear models to investigate the factors affecting cardiovascular disease
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.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".