Hybrid of Smart System Model to Support the Service of Fertility Doctors in Handling In-Vitro Fertilization Patient Complaints

Abstract

The majority of In-Vitro Fertilization (IVF) patients immediately call a fertility doctor when they experience different symptoms than usual. However, the high workload makes fertility doctors unable to immediately provide recommendations to handle complaints of IVF patients, while the longer wait for recommendations from fertility doctors will increase the anxiety of IVF patients and high levels of anxiety affect the success rate of IVF programs. The Case-Based Reasoning (CBR) model has lower performance than the modified CBR model, and the CBR model adds to the workload of fertility doctors, namely having to handle the revision stage. To overcome these problems, the CBR model was modified by applying the Chris Case-Based Reasoning (CCBR) similarity formula and combining it with the Rule-Based Reasoning model. The results of performance measurements showed that the accuracy score increased to 47\% and the precision score remained 100\%, so the results of this modification of the CBR model are worthy of being recommended for application to a smart system for handling complaints of IVF patients.