AI- Based Heart Disease Detection Using Machine Learning
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
Heart disease continues to be among the top causes of death globally, and early and precise diagnosis is required for proper treatment. Advances in artificial intelligence (AI) and machine learning (ML) in recent times have made it possible to create predictive models that can aid in early detection and risk prediction of heart disease. This work suggests a machine learning method of heart disease detection from patient health information, with clinical factors such as blood pressure, cholesterol levels, and lifestyle. A range of ML algorithms, from logistic regression and decision trees to support vector machines and deep models, were trained and tested for predictive performance. Feature selection methods were used to optimize model performance and interpretability. Experimental findings show that AI-based models are capable of high accuracy, sensitivity, and specificity in identifying heart disease with better performance than conventional diagnostic strategies. The paper identifies the usefulness of AI-driven decision support systems in medicine for assisting clinicians with early diagnosis and enhancing patient care. Future directions will include real-time deployment, model interpretability, and coupling with electronic health records for deployment in clinical practice.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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 it