Towards comparing and using Machine Learning techniques for detecting and predicting Heart Attack and Diseases
Bibliographic record
Abstract
Heart diseases are one of the deadly but are silent killers for humans, which results in the increase in death rate of sufferers every year. The World Health Organization (WHO), in the year 2016, reported that 17.9 million deaths that occur worldwide per year are a result of heart disease. In the health care sector, enormous data are being generated on a daily basis, which contains different types of data, and acquiring knowledge from these data is essential. This knowledge can be acquired using various data mining techniques to mine knowledge by designing models from the medical records dataset. We implement a machine learning based system that can detect and predict heart diseases in patients using the medical records of patients. The proposed solution is based on existing techniques like Random Forest Bayesian Classification and Logistic Regression, which provides a decision support system for medical professionals to detect and predict heart diseases and heart attacks in humans or individuals using risk factors of heart disease. The dataset used in our model consists of 18 features (risk factors) and 1990 observations after performing preprocessing. It was then split into 80% train sets and 20% test sets. Using real medical records of patients, a series of experiments were conducted to examine the performance and accuracy of the proposed system. The system was implemented in RStudio platform which predicts the risk of heart disease in patients. The compared results showed that the system performance and accuracy are acceptable with heart disease classification accuracy of 92.44% for Random Forest, 61.96%, and 59.7% for Naïve Bayes Classifier and Logistic Regression, respectively.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".