An efficient patient’s response predicting system using multi-scale dilated ensemble network framework with optimization strategy

The forecasting of a patient's response to radiotherapy and the likelihood of experiencing harmful long-term health impacts would considerably enhance individual treatment plans. Due to the continuous exposure to radiation, cardiovascular disease and pulmonary fibrosis might occur. For forecasting the response of patients to chemotherapy, the Convolutional Neural Networks (CNN) technique is widely used. With the help of radiotherapy, cancer diseases are diagnosed, but some patients suffer from side effects. The toxicity of radiotherapy and chemotherapy should be estimated. For validating the patient's improvement in treatments, a patient response prediction system is essential. In this paper, a Deep Learning (DL) based patient response prediction system is developed to effectively predict the response of patients, predict prognosis and inform the treatment plans in the early stage. The necessary data for the response prediction are collected manually. The collected data are then processed through the feature selection segment. The Repeated Exploration and Exploitation-based Coati Optimization Algorithm (REE-COA) is employed to select the features. The selected weight features are input into the prediction process. Here, the prediction is performed by Multi-scale Dilated Ensemble Network (MDEN), where we integrated Long-Short term Memory (LSTM), Recurrent Neural Network (RNN) and One-dimensional Convolutional Neural Networks (1DCNN). The final prediction scores are averaged to develop an effective MDEN-based model to predict the patient's response. The proposed MDEN-based patient's response prediction scheme is 0.79%, 2.98%, 2.21% and 1.40% finer than RAN, RNN, LSTM and 1DCNN, respectively. Hence, the proposed system minimizes error rates and enhances accuracy using a weight optimization technique.