Prediction of survival probability of cancer using machine learning models

Abstract

As one of the major diseases, cancer has always been a hidden danger to human health. There has been a considerably improvement in the therapy for patients all over the world, as research and technology advance, medical care becomes more effective. In this regard, the cure rate and survival probability have increased positively compared with the last century. However, the incidence rate of cancer has not been effectively controlled, and lung cancer and breast cancer are still more common. Predicting the probability that a cancer patient will survive at their initial appointment is extremely important according to this report. In this case, doctors can not only have a more detailed understanding of the situation of patients, but also make the allocation of medical resources more reasonable; Secondly, it can also promote the improvement of medical treatment in cancer. This article will first import the relevant data sets and analyze the variables contained. Then, the next step will use logistic regression analysis and linear regression analysis to predict the survival probability of patients. Furthermore, completed the judgement which variable has a greater impact by comparing the data that affect this probability. By comparing the accuracy of these regression analysis, the accuracy of logical regression (93.14%) is higher than that of linear regression (77.12%). In this case, logistic regression analysis will be more applicable. Finally, this paper compares the influence of related variables. According to the findings, a patient's probability of survival is determined by the amount of lymph nodes inside the system.