Fuzzy Inference with Enhanced Convolutional Neural Network Based Classification Framework for Predicting Heart Attack Using Sensor Data
Bibliographic record
Abstract
Heart attack is becoming a common life-threatening disease in the current lifestyle of the people. There is a lack of an automatic mechanism to detect it using medical sensor data. Because processing medical sensor data is tedious and leads to more processing overhead. Hence, we are proposing a framework to process the data by mining the important patterns in it i.e., we designed a framework to process the data sensed by various sensors that can be deployed on the human body in the form of gadgets using the technologies like Internet of Things and Machine learning. This paper presents a Fuzzy Inference with a Modified Convolutional Neural Network framework for heart attack prediction. We trained and tested our designed framework on the medical sensor data and achieved good prediction accuracy. This framework offers a foundation for developing a system of decision support that has the potential for learning and the ability to cope with disease management vagueness and unstructuredness. The result is compared with a few existing methods like SVM, K-Nearest Neighbor, Logistic Regression, and Convolutional Neural Network to show improved classification accuracy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".