Modified Case-Based Reasoning Model Helps Fertility Subspecialist Doctor Handle the Revision Stage and Answer Accurately In-Vitro Fertilization Program Patient’s Questions

Eighty two percent of in-vitro fertilization (IVF) program patients immediately ask fertility subspecialist doctors via SMS/Whatsapp messenger app when they feel different symptoms than what they usually feel. However, the high workload has been fertility subspecialist doctors’ obstacle to immediately reply these IVF program patients’ anxious questions. On the other hand, the long time it takes to receive a reply increases IVF program patients’ anxiety and, in turn, the high anxiety affects the success rate of IVF program, making it all the more important to help fertility subspecialist doctors deal with their obstacles. The weakness of Case-Based Reasoning (CBR) model is that the recommendation accuracy score is lower than that of modified CBR model and it makes the workload of fertility subspecialist doctors greater who have been busy dealing with the revision stage. To overcome this weakness, the Chris Case-Based Reasoning (CCBR) similarity formula is applied to CBR model and it combines with the Rule-Based Reasoning (RBR) model. The measurement of Confusion Matrix in the performance of CBR model combination which uses the CCBR similarity formula with RBR model suggests that the accuracy score increases to 47% and the precision score remains 100%, hence this model is declared capable of helping fertility subspecialist doctors handle the revision stage and accurately answer IVF program patients’ anxious questions.The limited scope of research makes it impossible for this research finding to be used as a standard model just yet for it needs to be tested for a wider scope.