Our aim is to investigate the added value of automated machine learning (AutoML) for potential future applications in cancer diagnostics. Using two important diagnostic questions, non-invasive determination IDH mutation status and ATRX status, we analyze whether it possible use AutoML develop models that are comparable performance conventional (ML) developed by experts. For this purpose, using ...