Leveraging Neighbourhood Component Analysis for Optimizing Multilayer Feed-Forward Neural Networks in Heart Disease Prediction
Bibliographic record
Abstract
The prevalence of heart disease, often exacerbated by unhealthy lifestyle choices, necessitates timely and accurate diagnosis-a task that can often be laborious for medical professionals.Over time, machine learning algorithms have demonstrated significant promise in facilitating efficacious predictions within healthcare settings.This study introduces a novel automatic classification model, Neighbourhood Component Analysis Optimized Multilayer Feed-Forward Neural Network (NCA-MLFFNN), designed to predict the onset of heart disease.The first phase of the proposed model employs Neighbourhood Component Analysis (NCA), a technique that aids in selecting the optimal features from the neighbourhood.Subsequently, a robust Multilayer Feedforward Neural Network (MLFFN) model is developed to train these selected features.A notable innovation in this work is the unique approach to data partitioning.Breaking away from the traditional practice of randomly dividing data into training and testing sets, the proposed model employs NCA for strategic data splitting.To enhance the precision of classification and minimize the misclassification rate, a backpropagation algorithm is employed.The study also includes a comparative analysis of various machine learning algorithms to gauge their performance.Experimental results suggest that the NCA-MLFFNN model outperforms other models, achieving an impressive accuracy of 96.03%.This paper, therefore, underscores the potential of NCA-MLFFNN as an effective tool in predicting heart disease and facilitating early intervention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".