Detection of Heart Disease Using Binary Classification Machine Learning Model
Bibliographic record
Abstract
Heart disease is a significant global health issue, causing millions of deaths annually.Despite advancements in medical technology, early and accurate diagnosis remains challenging.This study aims to detect heart diseases using binary classification machine learning models.The methodology employed a Heart Failure Prediction Dataset from Kaggle, with no issues of duplicates, missing data, outliers, or multicollinearity.Five machine learning models, including K-Neighbor Classifier, decision tree, support vector machine, random forest, and logistic regression, were trained and tested.The random forest model with hyper-parameters 'n_estimators': list (range (5,40,3)), 'max_features': ['log2', 'sqrt'] yielded the highest accuracy rate of 87.5%, precision rate of 90.4%, recall rate of 87.9%, f1_score of 89.1%, and auc_score of 93.6%.These results indicate that the random forest model has a notable capacity for accurate heart disease prediction, offering potential benefits such as reduced mortality rates and improved patient outcomes.Further research is recommended to establish standard data collection and analysis methods and to develop prediction models that consider the unique characteristics of diverse populations.
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.
How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".