  We propose predictive models that estimate GBM patients' health status of
one-year after treatments (Classification task), predict the long-term
prognosis of GBM patients at an individual level (Survival task). We used total
of 467 GBM patients' clinical profile consists of 13 features and two follow-up
dates. For baseline models of random forest classifier(RFC) and random survival
forest model (RSF), we introduced generalized linear model (GLM), support
vector machine (SVM) and Cox proportional hazardous model (COX), accelerated
failure time model (AFT) respectively. After preprocessing and prefixing
stratified 5-fold data set, we generated best performing models for model types
using recursive feature elimination process. Total 10, 4, and 13 features were
extracted for best performing one-year survival/progression status RFC models
and RSF model via the recursive feature elimination process. In classification
task, AUROC of best performing RFC recorded 0.6990 (for one-year survival
status classification) and 0.7076 (for one-year progression classification)
while that of second best baseline models (GLM in both cases) recorded 0.6691
and 0.6997 respectively. About survival task, the highest C-index of 0.7157 and
the lowest IBS of 0.1038 came from the best performing RSF model while that of
second best baseline models were 0.6556 and 0.1139 respectively. A simplified
linear correlation (extracted from LIME and virtual patient group analysis)
between each feature and prognosis of GBM patient were consistent with proven
medical knowledge. Our machine learning models suggest that the top three
prognostic factors for GBM patient survival were MGMT gene promoter, the extent
of resection, and age. To the best of our knowledge, this study is the very
first study introducing a interpretable and medical knowledge consistent GBM
prognosis predictive models.
