An explainable lattice based fertility treatment outcome prediction model for telefertility

Abstract

The global trends of women in the reproductive age have significantly altered due to their personal and career development engagements besides adoption of contraceptive methods. Since women are extending birth to their late ages where natural conception is quite hard besides other factors, it has globally boosted the fertility service market which is a projected 41.4 billion industry by 2026. Despite the growing market for fertility services, infertility evaluation is still uncomfortable, expensive, inaccessible and ambiguous for both the customers and the fertility service providers. In this work, we deploy Machine Learning and Explainable Artificial Intelligence to predict the outcomes of fertility treatment using interpretable Machine Learning Lattice Models for predictive, preventive and precision reproductive medicine. We also introduce the concept of Quantum Lattice Learning in Artificial Intelligence for Machine Learning Interpretability.