Prostate cancer forecasting in small samples based on lightweight neural networks using ensemble learning
Bibliographic record
Abstract
Prostate cancer is the most common malignancy among Australian men, with over 20 000 new diagnoses each year. Accurate forecasts of its incidence and mortality inform stakeholder decision-making and help mitigate its public health impact. In this context, we introduce cutting-edge lightweight neural networks into the domain of prostate cancer data forecasting with edge intelligence for the first time. To address the issue of overfitting in coarse-grained and small-scale prostate cancer datasets, we employ structurally streamlined models: the Gated Recurrent Unit (GRU) and Temporal Convolutional Network (TCN), representing two predominant branches of neural networks. The GRU’s simplified gating mechanism maintains excellent long-term dependencies capturing capability while drastically reducing parameter count, and the TCN combines sparse connections, parameter sharing, and causal dilated convolutions for efficient temporal modeling. To further bolster generalization, we integrate multiple regularization strategies, including the snapshot ensemble method. Comparative experiments on three real-world prostate cancer datasets demonstrate that our improved lightweight, high-performance neural networks achieve over 40% higher accuracy than linear time series forecasting suitable for small-scale datasets.
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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 | 0.005 |
| 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".