Prediction of Breast Cancer Diseases From Genes Using Pso and Cso Tuned Long Short-term Memory

Abstract

Gene data set collected from a diverse population gene expression profiles, genetic variations, and clinical attributes for earlier detection cancer. Time Series Forecasting (TSF) techniques are applied and exploits temporal dependencies within the gene data, enables the prediction of breast cancer and progression. The proposed methodology such as Particle Swarm Optimization-Long Short Term Memory (PSO & LSTM) and Cat Swarm Optimization -Long Short Term Memory (CSO & LSTM) combines with gene data augmentation and analyse the temporal patterns breast cancer genes. Receiver Operating Characteristic (ROC) curve is used for evaluation the proposed models predictive performance. The proposed methods are validated in traditional dataset and collected gene data sets, from National Center for Biotechnology Information (NCBI). The results are compared with existing classification model and evaluated the effectiveness of the TSF methods such as of CSO-LSTM and PSO-LSTM in prediction of breast cancer diseases. The proposed methods contribute to early detection by leveraging time series forecasting techniques. The proposed model improves the accuracy of and reliability of breast cancer prediction, which enables health professional with more information and potentially enhances the patient outcomes