A novel hybrid model of flying geese optimization and attention-LSTM for predicting survival outcomes in clear cell renal cell carcinoma

Clear Cell Renal Cell Carcinoma (ccRCC) is the most aggressive and metastatic subtype of renal cell carcinoma and also the type with the highest mortality rate. To enhance survival prediction accuracy and facilitate informed clinical decision-making, this study presents a hybrid model that combines the Flying Geese Optimization Algorithm (FGOA) with an attention-based Long Short-Term Memory (A-LSTM) network. The proposed framework is trained and evaluated using data from the Cancer Genome Atlas Kidney Clear Cell Carcinoma (TCGA-KIRC) database. The feature selection process employed seven representative optimization algorithms covering evolutionary, swarm intelligence, and bio-inspired paradigms. The selected features were then analyzed using the attention-based A-LSTM network to predict survival outcomes in patients with ccRCC. Evaluation metrics for model performance included accuracy, precision, recall, and F1 score. The results showed that the FGOA-A-LSTM model performed best, with an accuracy of 80.8%, precision of 81.5%, recall of 86.9%, and F1 score of 84.1%, outperforming the other models. This result also indicates that on imbalanced datasets, the F1 score may be higher than the accuracy. Furthermore, Cox proportional hazards regression analysis showed that survival outcomes were significantly correlated with factors such as gender, tumor stage, previous treatment, and treatment method. This study introduces an innovative FGOA-A-LSTM framework that improves survival prediction in ccRCC. By integrating optimization-driven feature selection with an attention-enhanced deep learning architecture, the work makes a contribution to improving clinical risk assessment.