A Survey on Machine Learning in Forecasting Success in Intrauterine Insemination

Abstract

This survey paper aims to provide a comprehensive overview of the current state of research regarding the utilization of machine learning in forecasting the success of Intrauterine Insemination procedures (IUI). The discussion commences with an exploration of the fundamental concepts and advantages in IUI, as well as the challenges associated with predicting success. Next, the various Machine Learning approaches, including supervised, unsupervised, and deep learning techniques utilized in this context, are explored. Furthermore, an examination is conducted on the critical factors and features considered in the development of predictive models, such as patient age, hormonal profiles, cycle characteristics, and sperm analysis. Additionally, it is found that there may be potential benefits in incorporating, lifestyle, emotional, yoga and meditation factors into predictive models to enhance their accuracy.