Squirrel Search Deer Hunting-Based Deep Recurrent Neural Network for Survival Prediction Using PAN-Cancer Gene Expression Data. (3rd November 2021) - Majji, Ramachandro; Rajeswari, R; Vidyadhari, Ch; Cristin, R
Abstract: This paper devises a novel technique, namely Squirrel Search Deer Hunting-based deep recurrent neural network (SSDH-based DRNN) for cancer-survival rate prediction using gene expression (GE) data. Initially, the input GE data are transformed using the polynomial kernel data transformation. Then entropy-based Bayesian fuzzy clustering is employed for gene selection. Then, the selected features are strengthened through survival indicators based on time series data features, like simple moving average (SMA) and rate of change. Finally, the survival rate prediction is performed using a deep recurrent neural network (DRNN), in which the training is carried out with squirrel search deer hunting (SSDH). The proposed SSDH algorithm is devised by combining Squirrel Search Algorithm (SSA) and deer hunting optimization algorithm (DHOA). The performance of the proposed methodology is analyzed using Pan-Cancer (PANCAN) dataset with a prediction error of 4.05%, RMSE of 7.58, the accuracy of 90.98%, precision of 90.80%, recall of 92.03% and F1-score of 91.41%. The devised method with higher prediction accuracy and the lower prediction error is employed for the cancer survival prediction of the patients for the cancer prognosis. Besides, it will be helpful for the clinical management of cancer patients.