Towards Early Diagnosis and Intervention: An Ensemble Voting Model for Precise Vital Sign Prediction in Respiratory Disease
Bibliographic record
Abstract
Worldwide, cardiovascular and chronic respiratory diseases have approximately million deaths each year. Evidence indicates that the ongoing COVID-19 pandemic directly contributed to increase blood pressure, cholesterol, and blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aimed to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies medical professionals when necessary. Utilizing real-world data, a linear regression model inspired by the Facebook Prophet model was utilized to predict vital signs for the next 180 seconds. With lead time of 180 seconds, medical professionals can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, Naïve Bayes classification model, Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outperforms previous attempts for vital sign prediction. Compared with alternative methods, the Facebook Prophet model had the mean sqaure error for predicting vital signs. Hyperparameter tunning was utilized to refine model, yielding improved short- and long-term outcomes for each vital sign. From the results, it showed that the designed model acheives higher F-measure performance. The incorporation of additional elements, such as momentum indicators, can increase the flexibility of the model with calibration. The findings of this study demonstrated that the proposed model is more accurate in predicting vital signs and trends.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".