Squeeze RNN with hybrid optimization: A novel approach for heart disease prediction using gene expression data

Abstract

Heart disease is a leading cause of death, imposing a considerable strain on individuals, families, and the healthcare system. Its onset is often linked to factors such as inadequate attention to diet, health, and lifestyle. Prompt diagnosis and early treatment are vital to enhancing patient survival and reducing mortality. In this work, a hybrid Deep Learning (DL) method named Squeeze Recurrent Neural Network with Political Deep Hunting Optimization (PDHO_SqueezeRNN) is proposed using gene expression data for detecting heart disease, where SqueezeRNN combines the SqueezeNet model and Recurrent Neural Network (RNN). At first, input data is forwarded to data transformation, which is achieved by Yeo Johnson transformation. Then, feature fusion is performed by Lorentzian metric and Deep Neural Network (DNN). Finally, heart disease is detected by the proposed PDHO_SqueezeRNN. By combining Political Optimizer (PO) and Deer Hunting Optimization (DHO), the PDHO algorithm is developed. Moreover, PDHO_SqueezeRNN achieved peak values of 95.20% for accuracy, 97.00% for sensitivity, and 95.90% for specificity, surpassing existing approaches.