Fuzzy Rule Based- Model for Proficient Heart Disease Prediction in Data Lake
Bibliographic record
Abstract
Cardiovascular diseases remain the leading cause of mortality on a global scale.Currently, healthcare organizations are shifting their operational strategies to enhance efficiency and reduce costs.For this transition, the integration of analytics into IT strategy is imperative.Data lakes, which aggregate data from multiple sources and employ analytical models, provide an innovative approach to information management, reporting, and predictive analytics.These models enable the production of advanced analytical insights, the implementation of evidence-based care plans, and the improvement of patient engagement outcomes, thus setting the stage for IoT-based prognostic systems aimed at reducing mortality rates.The present research proposes a comprehensive data analysis for the prognosis of coronary heart disease, a task that poses considerable challenges due to the volume of data across various disciplines and the complexity involved in analyzing, extracting, managing, and configuring data with massive data technologies and tools.To tackle this challenge, a multi-level fuzzy rule generation is suggested for identifying the features used in heart disease prediction.These features are then trained using an optimized recurrent neural network.The features are classified into labeled classes based on the risk assessment of a medical professional, enabling the prediction of the class based on risk.Early diagnosis and treatment are thus facilitated.When benchmarked against traditional systems, the proposed approach demonstrates superior performance, validating its potential for efficient heart disease prediction.
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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 | 0.003 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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".