Multi-Disease Risk Prediction: Leveraging Machine Learning for Heart Disease and Diabetes Prediction
Bibliographic record
Abstract
Heart disease is one of the leading causes of death worldwide, affecting primarily middle aged and older adults, and men are more likely to be affected than women. The International Diabetes Federation also states that there are 382 million people with diabetes worldwide. That number is also expected to rise to 592 million by the year of 2035. In this research study we will present a machine learning model that predicts heart disease and diabetes using data from Kaggle, Data World and the UCI repository. We were operating under the empirical goal of developing a model that could help us when attempting to predict heart disease and diabetes, so that fewer human beings die from each respectively. Our aim contrived the development of a model that is expected to gain a higher percentage of accuracy by combining the results of several machine learning techniques. The machine learning techniques used were KNN, logistic regression, random forest, SVM and finally gradient- boosting. The ultimate conception of our research study was to determine the best model to use when predicting heart disease and diabetes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".