Survival Analysis on Lung Cancer Patients in Damaturu-Nigeria

Abstract

Cancer is one of the leading causes of death by lung cancer in particular, is the leading cause of cancer death in both men and women, accounting for 23% of all cancer deaths in 2019 according to the Centers for Disease Control and Prevention. One particular problem with lung cancer is that it usually has a poor prognosis. With such a deadly disease, it is crucial to predict the survival likelihood of cancer patients. However, this is not an easy task due to the many factors affecting the disease progression. Survival time has become an essential outcome of clinical trial, which began to emerge among the latter half of the 20th century. A present study was carried out on the survival analysis for patients with lung cancer. The data was obtained from Yobe State Specialist Hospital, Damaturu where each sample was collected from the recipients of the treatment of radical prostatectomy. The Kaplan Meier method was used to obtain and estimate the survival function and median. The log-rank test was used to test the differences in the survival curves. The cox proportional hazard (PH) model provided an effective covariate on the hazard function. As a result of cox PH model, the influence of standard clinical prognostic factors is based on the hazard rate of lung cancer patients. We performed rigorous cross-examination on each feature's relationship and the model for each feature type using data analysis information and survival analysis models. For each feature type, we used one representative survival analysis model from semi-parametric methods (Cox proportional hazards model), one from non-parametric methods (Kaplan-Meier estimator), and one from machine learning approaches (random survival forests). Using the results obtained from these different methods, we identified the best feature types and model combinations to get the top performance for various follow-up periods. The best model is Cox proportional hazards model based on the AIC and log-likelihood functions respectively.