Prostate cancer forecasting in small samples based on lightweight neural networks using ensemble learning

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.