A new approach for cancer prediction based on deep neural learning

Abstract

We know today that numerous factors play a significant role as causes of cancer. Because of this, a doctor's opinion alone cannot be used to classify cancer. Intelligent algorithms providing medical assistance are therefore necessary. In addition, many researchers have adopted them for estimating the likelihood of patient survival, and others have employed predictive methodologies like machine learning and deep learning to forecast prognoses for cancer. The accuracy of predictive cancer prognosis is currently of widespread concern. Since deep neural learning (DNL) methods can quickly predict outcomes from a significant amount of clinical and genetic data, they are essential for predicting various diseases. Deep neural learning is the foundation of our suggested approach. Our deep neural learning cancer prediction model (DNLC) has the following stages. In the first stage, Deep Network (DN) is used to select the best collection of features from datasets. In the second stage, we train genomic or clinical data samples with a deep neural network (DNN). In the third stage, we evaluate the capabilities of the DNLC model of predicting cancer in its earlier stages. For classification, DNLC uses five cancer datasets, which are for colon, lung adenocarcinoma, squamous cell carcinoma, breast, and leukaemia cancers. The five cancer datasets are used in experiments to predict how well the suggested model will perform. The dataset is divided into two parts: training sets, which make up 80% of the dataset, and testing sets, which make up 20%. The experimental results show that the suggested model performs better in terms of accuracy than earlier CNN and RNN models. Our findings demonstrate that the DNLC technique, with an average accuracy of 93%, outperforms other methods in all circumstances.