Improving Heart Disease Prediction Accuracy through Machine Learning Algorithms
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
This study explores the application of a range of machine learning and deep learning techniques for predicting cardiovascular diseases. Various models, including Random Forest, Logistic Regression, Gradient Boosting, AdaBoost, Support Vector Machine (SVM), XGBoost, and both Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks are evaluated. A comprehensive evaluation is conducted by considering supplementary metrics, refining hyperparameter tuning, assessing feature importance using SHAP, comparing traditional machine learning with deep learning approaches, and examining the clinical relevance. It concludes that XGBoost achieves the highest accuracy (88%), and notes that CNN and LSTM may prove beneficial with larger datasets. Moreover, the study investigates the practical applications of these models, focusing on their potential integration into clinical decision support systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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