Prediction method for survival time of patient with breast cancer based on deep neural network

Abstract

The invention relates to a prediction method for the survival time of a patient with breast cancer based on a deep neural network. The method comprises the following steps: 1) acquiring data, whereinthe data is clinical data and omics data, and the omics data comprises gene expression data and DNA methylation data; 2) preprocessing the data; 3) extracting the features of a data set; and 4) constructing a deep neural network. According to the invention, survival time prediction is carried out by fusing the deep neural network with the multi-omics data of breast cancer; clinical data, gene expression data and DNA methylation data of breast cancer are obtained from a TCGA database, data features are extracted, deep neural network models are constructed respectively, and then back-end fusionis performed, so the performance of survival time prediction for breast cancer is improved, and a lifetime prediction model is obtained; and the method has a remarkable effect in prediction of the survival time of patients with breast cancer.

Description

technical field [0001] The invention belongs to the field of biomedical engineering and relates to a method for predicting the survival period of breast cancer based on a deep neural network. Background technique [0002] Breast cancer is one of the most common malignant tumors in women. According to statistics, there are about 1.2 million new female breast cancer patients worldwide every year, and about 500,000 women die of breast cancer every year. According to research, breast cancer is a disease that easily causes metastasis. About 50% of cases can be cured after surgical treatment, and recurrence or metastasis may occur in the remaining 50% of cases. With the increasing incidence of breast cancer, accurate prognosis prediction for cancer patients is the key to the current cancer problems. Prognosis refers to predicting the possible course and outcome of a disease, including not only predicting the possibility of a certain outcome within a certain period of time, but al...

AI Technical Summary

Problems solved by technology

However, the current mainstream direction of machine learning in cancer prediction and diagnosis is early-stage disease-assisted screening. Postoperative cancer treatment mainly depends on doctors’ regular follow-up visits, and patients regularly go to the hospital for reexamination, which greatly reduces the real-time and convenience of postoperative treatment plans. If machine learning is used to predict the postoperative survival period of breast cancer patients, it will help to screen high-risk patients who may need preventive treatment and carry out targeted postoperative preventive treatment to improve the postoperative survival rate of patients

Images