Machine Learning Approaches for Estimating Prevalence of Undiagnosed Hypertension among Bangladeshi Adults: Evidence from a Nationwide Survey
Bibliographic record
Abstract
In South Asia, hypertension is the most prevalent modifiable risk factor for cardiovascular disorders. Comparing machine learning to statistical approaches, it has been found that it performs better at identifying clinical risk. This study utilized machine learning techniques to estimate undiagnosed hypertension. We created a single dataset out of individual-level data from the Bangladesh Demographic and Health Survey (2017-18). The JNC-7 and ACCAHA criteria were used to define hypertension. We used two well-known ML approaches logistic regression and log-binomial regression to determine the prevalence of undiagnosed hypertension. A considerable number (16%) of hypertension cases in Bangladesh are still undiagnosed. Young people and the divisions of Sylhet and Rangpur were found to be more at risk for undetected hypertension. ML models performed well at identifying undiagnosed hypertension and its contributing factors in South Asia. Future studies incorporating biological markers will be necessary to improve the ML algorithms and determine their applicability.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".