Heart disease prediction using machine learning
Bibliographic record
Abstract
The wide adaptation of computerbased technology in the health care industry resulted in the accumulation of electronic data. Due to the substantial amounts of data, heart disease is the major cause of deaths worldwide. To give treatment for heart disease, a lot of advanced technologies are used. In medical center it is the most common problem because many medical persons do not have equal knowledge and expertise to treat their patient, so they deduce their own decision and as a result it shows poor outcome and sometimes lead to death. To overcome these problems, prediction of heart disease is being done by using machine learning algorithms and data mining techniques, it has become easy to perform automatic diagnosis in hospitals as they are playing vital role in this regard. However, supervised machine learning (ML) algorithms have showcased significant potential in surpassing standard systems for disease diagnosis and aiding medical experts in the early detection of high-risk diseases. In this literature, the aim is to recognize trends across various two types of supervised ML models in disease detection through the examination of performance metrics. The most prominently discussed supervised ML algorithms were Native Bayes (NB), Decision Trees (DT). Native Bayes (NB)is the most adequate at detecting kidney parameters of disease based on blood report of patient. The Naive Bayes (NV),Decision Tree(DT) performed highly at the prediction of heart diseases (Heart attack or Heart Burn).We have used different parameters to predict heart disease. Those parameters are Age, Gender, Cerebral palsy (CP), Gender, Cerebral palsy (CP), Blood Pressure (bp), Fasting blood sugar test (fbs) etc. In our research paper, we have used built in dataset. This paper investigates which technique gives more accuracy in predicting heart disease based on health parameters. Experiment shows that Naïve Bayes has the highest accuracyof 86%.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".