Cancer Prediction Using Feature Fusion and Taylor-TSA-Based GAN with Gene Expression Data

Abstract

This research paper develops an efficient model, named Taylor-Tunicate Swarm Algorithm-based Generative Adversarial Networks (Taylor-TSA-based GANs) for cancer prediction. The developed Taylor-TSA incorporates the Taylor series with Tunicate Swarm Algorithm (TSA) algorithm. The Yeo–Johnson (YJ) transformation is employed for the data transformation. The feature fusion is evaluated by Deep Stacked Autoencoder (Deep SAE). The fused feature is given as input to the cancer prediction done by GAN trained by Taylor-TSA. The developed model is an effective and efficient use of information with clinical data. The Taylor-TSA-based GAN is analyzed in terms of accuracy, False Positive Rate (FPR), and True Positive Rate (TPR) with the values of 0.9184, 0.1782, and 0.9246.