Gaussian-Stacking Multiclassifiers for Human Embryo Selection

ABsTRAcT Infertility is currently considered an important social problem that has been subject to special interest by medical doctors and biologists. Due to ethical reasons, different legislative restrictions apply in every country on human assisted reproduction techniques such as in-vitro fertilization (IVF). An essential problem in human assisted reproduction is the selection of suitable embryos to transfer in a patient, for BLOCKINwhich BLOCKINthe BLOCKINapplication BLOCKINof BLOCKINartificial BLOCKINintelligence BLOCKINas BLOCKINwell BLOCKINas BLOCKINdata BLOCKINmining BLOCKINtechniques BLOCKINcan BLOCKINbe BLOCKINhelpful BLOCKINas decision-support BLOCKINsystems. BLOCKINIn BLOCKINthis BLOCKINchapter BLOCKINwe BLOCKINintroduce BLOCKINa BLOCKINnew BLOCKINmulti-classification BLOCKINsystem BLOCKINusing BLOCKINGaussian networks to combine the outputs (probability distributions) of standard machine BLOCKINlearning BLOCKINclassification algorithms. Our method proposes to consider these outputs as inputs for a superior-level and to apply a BLOCKINstacking BLOCKINscheme BLOCKINto BLOCKINprovide BLOCKINa BLOCKINmeta-level BLOCKINclassification BLOCKINresult. BLOCKINWe BLOCKINprovide BLOCKINa BLOCKINproof BLOCKINof BLOCKINthe BLOCKINvalidity BLOCKINof the BLOCKINapproach BLOCKINby BLOCKINemploying BLOCKINthis BLOCKINmulti-classification BLOCKINtechnique BLOCKINto BLOCKINa BLOCKINcomplex BLOCKINreal BLOCKINmedical BLOCKINproblem: BLOCKINThe selection of the most promising embryo-batch for human in-vitro fertilization treatments.