The prediction and feature importance analysis of stroke based on the machine learning algorithm
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
The prediction of the probability of the patients’ stroke is a challenging task in the past decades. This study aims to predict the probability of stroke in patients using machine learning algorithms. Logistic regression model was used in this study to build the prediction model. In addition, the data preprocessing technology e.g. missing value processing and feature encoding was also carried out. The dataset collected from the Kaggle Platform used for the analysis contains various clinical and demographic features of the patients. The model achieved an accuracy of 96.3% in predicting stroke probability. Furthermore, the feature importance analysis was conducted to identify the most significant features that contribute to the prediction. The results demonstrated that some features such as age, glucose level, work type and hypertension were the most important features for predicting stroke probability. The findings of this study could help healthcare professionals in identifying high-risk patients and providing timely interventions to prevent stroke occurrence.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it