A Novel Framework for Deep Convolutional Neural Network-Based Heart Failure Disease Prediction Using an Optimized Efficient net-B0 Model
Bibliographic record
Abstract
Convolutional neural networks (CNNs) have been widely used in medical decision support systems to accurately predict and diagnose various diseases.Because of their ability to identify relationships and hidden patterns in healthcare data, CNNs have been extremely successful in developing health support systems.One of the most important and useful application systems is in the prediction of Heart Failure Diseases (HDFs) by observing cardiac anomalies.Fundamentally, CNNs have multiple hyper parameters and various specific architectures, which are costly and impose challenges in selecting the best value among possible hyper parameters.Furthermore, CNNs are sensitive to hyper parameter values, which have a significant impact on the efficiency and behavior of CNN architectures.Datasets from Electronic Health Records (EHRs) have recently been used to diagnose a variety of diseases, including heart failure.In this paper, we proposed the Deep convolutional neural network algorithm (DCNN), which is one of the deep learning algorithms that has been successfully used to solve computer vision problems.In our work, EfficientNet-B0 is a type of DCNN model that is used with a transfer learning approach to recognize diseases in Heart Failure images.To determine the effect of transfer learning with fine tuning, we assessed the performance of all EfficientNet-B0 variants on this imbalanced multiclass classification task using metrics such as Specificity, Recall, Accuracy, F1-measure, and Confusion Matrices.However, the accuracy and parameters of EHRs-based HFDs diagnosis are limited by the lack of an appropriate feature set.The experimental results show that EfficientNet-B0 achieves higher accuracy 98.45% with fewer parameters than the five classical DCNN models, demonstrating that the DCNN-EfficientNet-B0 model achieves more competitive results on HFDs identification.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".