Risk Factors Categorizations of Ischemic Heart Disease in South-Western Bangladesh
Bibliographic record
Abstract
Ischemic heart disease (IHD) is one of the leading causes of death worldwide. However, different geographic regions show different variations of the risk factors of this disease based on the different lifestyles of people. This study examines the current IHD condition in southern Bangladesh, a Southeast Asian middle-income country. The main approach to this research is an AI-based proposal of a reduced set of the greatest impact clinical traits that may cause IHD. This approach attempts to reduce IHD morbidity and mortality by early detection of risk factors using the reduced set of clinical data. Demographic, diagnostic, and symptomatic features were considered for analysing this clinical data. Data pre-processing utilizes several machine learning techniques to select significant features and make meaningful interpretations. A proposed voting mechanism ranked the selected 138 features by their impact factor. In this regard, diverse patterns in correlations with variables, including age, sex, career, family history, obesity, etc., were calculated and explained in terms of voting scores. Among the 138 risk factors, three labels were categorized: high-risk, medium-risk, and low-risk features; 19 features were regarded as high, 25 were medium, and 94 were considered low impactful features. This research’s technological methodology and practical goals provide an innovative and resilient framework for addressing IHD, especially in less developed cities and townships of Bangladesh, where the general population’s socio-economic conditions are often unexpected. The data collection, pre-processing, and use of this study’s complete and comprehensive IHD patient dataset is another innovative addition. We believe that other relevant research initiatives will benefit from this work.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".