Using machine learning to predict hypertension from a clinical dataset
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
Hypertension is an illness that often leads to severe and life threatening diseases such as heart failure, thickening of the heart muscle, coronary artery disease, and other severe conditions if left untreated. An artificial neural network is a powerful machine learning technique that allows prediction of the presence of the disease in susceptible populations while removing the potential for human error. In this paper, we identify the important risk factors based on patients' current health conditions, medical records, and demographics. These factors are then used to predict the presence of hypertension in an individual. These risk factors are also indicative of the probability of a person developing hypertension in the future and can, therefore, be used as an early warning system. We present a neural network model for predicting hypertension with about 82% accuracy. This is good performance given our chosen risk factors as inputs and the large integrated data used for the study. Our network model utilizes very large sample sizes (185,371 patients and 193,656 controls) from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) data set. Finally, we present a literature study to show the use of these risk factors in other works along with experimental results obtained from our model.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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