Two-Stage Prediction of Comorbid Cancer Patient Survivability Based on Improved Infinite Feature Selection

The modeling of comorbid cancer patients' survivability has theoretical significance and practical needs. Cancer survivability prediction may provide guidance for clinical decision making and personalized medicine. The Surveillance, Epidemiology, and End Results(SEER) program provides large data sets suitable for analysis with machine learning methods. In this study, we consider survival prediction to be a two-stage problem. The first is to predict the five-year survivability of patients. For those whom the predicted outcome is `death', the second stage predicts the remaining survival time. Male and female comorbid cancer cases(male-genital and urinary cancer for men and breast and female-genital cancer for women) were identified from the SEER database and labeled. In the classification stage,the dataset was processed with improved infinite feature selection(Iinf-FS) and random undersampling-based data balancing. These two methods resolved the issues of biased data set and poor classification accuracy. In the lifespan prediction stage, unsupervised infinite feature selection (UinfFS) was applied. The results indicate that the proposed method is effective.