Design and application of tumor prediction model based on statistical method

Abstract

Two prediction models for tumor prediction based on logistic regression and BP neural network were proposed in this paper; a sensitivity analysis of risk factors was also conducted. The two protocols will be implemented in the R language and demonstrated with relevant lung cancer data. Additionally, the two models are compared, verifying their accuracy and feasibility. Finally, a function is combined with R language, which can quickly achieve the above two programs. It is set up to obtain the tumor prediction model and the risk factors on the degree of correlation of the size of the disease. This method is convenient and practical and we hope it can be applied in the early discovery of cancer during future medicine.

Prediction of 3‐month treatment outcome of IgG4‐DS based on BP artificial neural network

“…Among them, BP‐ANN is a multi‐layer feedforward network trained according to the error back‐propagation algorithm. In the medical field, BP‐ANN has been widely used in the fields of disease screening, auxiliary diagnosis, diagnosis, disease risk, and prognosis analysis [Liu et al., 2019; Duan et al., 2017; Yao et al., 2019; Shen, Fan, & Guo, 2017; Zhang, Li, Li, & Li, 2018]. In clinical work, doctors often need to judge the prognosis of a disease based on certain risk factors.…”