Using machine learning to predict hypertension from a clinical dataset
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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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".