Chronological horse herd optimization-based gene selection with deep learning towards survival prediction using PAN-Cancer gene-expression data

Cancer has always been one of the major hazards to human life which is also the most difficult part of human disease history. The death rate due to cancer is high. The prediction results are affected because of the major dissimilarities present in clinical results. Hence, it is necessary to enhance the accuracy of cancer survival pre-diction, which remains a challenging one. To defeat the challenges, this research devises a robust approach, named Deep Recurrent Neural Network-based Chronological Horse Herd Political Optimization (DRNN-based CHHPO) for survival prediction. Here, the gene selection is performed using the proposed Chronological Horse Optimization (CHO) by assuming the parameters of fitness, for example Minkowski distance plus Renyi entropy. The Horse Herd Optimization (HOA) and Chronological concept is merged to form the CHO. With the selected genes, the gene features are strengthened using technical indicators to enhance the overall process. Finally, survival prediction is completed by means of DRNN, which is trained by the CHHPO, which is the amalgamation of Political optimizer (PO) and CHO. Superior presentation with the Prediction Error (PE) and minimal Root Mean Square Error (RMSE) of 0.456 and 0.467 is accomplished by this developed technique.

Automated summary

Cancer is a major threat to human life and a difficult aspect of human disease history. Due to clinical result differences, accurate cancer survival prediction remains a challenge. To address this, a robust approach called Deep Recurrent Neural Network-based Chronological Horse Herd Political Optimization (DRNN-based CHHPO) is proposed. The approach utilizes gene selection using Chronological Horse Optimization (CHO) and strengthens gene features using technical indicators. Survival prediction is completed through DRNN trained by CHHPO, which combines Political optimizer (PO) and CHO. The developed technique achieves superior performance with a Prediction Error (PE) of 0.456 and minimal Root Mean Square Error (RMSE) of 0.467.