  Tens of millions of women suffer from infertility worldwide each year. In
vitro fertilization (IVF) is the best choice for many such patients. However,
IVF is expensive, time-consuming, and both physically and emotionally
demanding. The first question that a patient usually asks before the IVF is how
likely she will conceive, given her basic medical examination information. This
paper proposes three approaches to predict the cumulative pregnancy rate after
multiple oocyte pickup cycles. Experiments on 11,190 patients showed that first
clustering the patients into different groups and then building a support
vector machine model for each group can achieve the best overall performance.
Our model could be a quick and economic approach for reliably estimating the
cumulative pregnancy rate for a patient, given only her basic medical
examination information, well before starting the actual IVF procedure. The
predictions can help the patient make optimal decisions on whether to use her
own oocyte or donor oocyte, how many oocyte pickup cycles she may need, whether
to use embryo frozen, etc. They will also reduce the patient's cost and time to
pregnancy, and improve her quality of life.
