Applying Predictive Analytics in Identifying Key Risk Factors for Hypertension in Malawi: A Randomized Controlled Population Health Study
Bibliographic record
Abstract
This study investigates the primary risk factors for hypertension in Malawi using predictive analytics and the CARROT-BUS (Capacity Building, Accountability, Resources, Results, Ownership, Transparency – Bottom-Up Strategy) model as a guiding framework. Drawing on baseline data from a population-level control cohort study, multiple machine learning models—Logistic Regression, Random Forests, Support Vector Machines (SVMs), Neural Networks, and XGBoost—were applied to assess predictive performance. Among them, XGBoost achieved the highest accuracy (88%) and AUC-ROC (0.92), followed by Random Forest and Logistic Regression. Key predictors included age, body mass index (BMI), systolic blood pressure, physical inactivity, and high sodium intake. In parallel, qualitative data from focus group discussions (FGDs) provided contextual insights into community knowledge, attitudes, and barriers regarding hypertension prevention and care. Participants revealed widespread misconceptions about hypertension symptoms and causes, reliance on traditional medicine, inadequate infrastructure, and medication shortages. The CARROT-BUS model served as a lens to assess systemic enablers and constraints, emphasizing the importance of community ownership, transparent resource allocation, and sustainable intervention planning. This mixed-methods approach demonstrates the value of integrating machine learning with participatory community engagement to guide data-informed, culturally relevant public health strategies. While the cross-sectional nature of the baseline data limits causal inference, and some self-reported variables may reflect social desirability bias, the study offers actionable insights for improving hypertension control in low-resource settings. Future phases, including midline and endline assessments, will further evaluate the effectiveness and sustainability of the interventions. These assessments are critical for enabling causal inference and determining the longitudinal impact of the intervention on hypertension control.
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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 | 0.029 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".