An Entropy enabled Random Forest Neural Network Algorithm to Grade the Reproductive System for Efficient Early Detection of Infertility

The membrane that borders the uterus is called endometrium. When the liner leaves the uterus, a problem is evident. The main risks of infertility and other health issues can be substantially reduced if the primary cause of endometriosis is understood. As a result, the affected people can receive the right medical care and therapy. The suggested ensemble model performs better than traditional machine learning techniques. For effective implantation, there must be a dependency between the endometrium and the embryo at the blastocyst stage. Data mining method where information gathered from the endometrium/sub endometrium and their ability is assessed uses the endometrium as a site for embryo implantation. Using a typical rating system has certain drawbacks because there are so many irrelevant and unclear criteria. The usability and precision of scoring systems can also be increased using a number of artificial intelligence methods, including random forests and neural networks. This study coupled an advanced reproductive grading system with an entropy and random forest approach to define individuals with infertility according to their health conditions and choose more effective therapies.