Building a Cardiovascular Disease predictive model using Structural Equation Model & Fuzzy Cognitive Map
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
According to Public Health Agency of Canada, Cardiovascular Disease (CVD) is the leading cause of death among adult men and women. Various research works have applied machine learning/data mining algorithms to predict CVD, but these methods suffer from a) lack of transparency of the predictive model building, b) lack of capability to introduce human wisdom, and c) lack of sufficient data. In this paper we provide a novel approach to tackle these issues and design a very robust and reasonably accurate model. Our approach is based on Structural Equation Modeling (SEM) and Fuzzy Cognitive Map (FCM). We used Canadian Community Health Survey, 2012 data set to test our approach. The designed model has 79% area under the ROC curve and 74% accuracy. We have used only the 20 most significant attributes, but we believe that adding more attributes and having an expert heart specialist panel would further improve the accuracy of the system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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