Data Driven Optimization of IVF Treatment Protocols: A Machine Learning Approach

Infertility, defined as the inability to achieve pregnancy after 12 months of regular unprotected intercourse, affects individuals due to various reproductive system dysfunctions. Fertility treatments, including assisted reproductive technologies (ART) like in vitro fertilization (IVF), offer hope by addressing factors such as blocked fallopian tubes, endometriosis, and male factor infertility. IVF involves stimulating egg production, retrieving eggs, fertilizing them in a lab dish, and transferring the resulting embryos to the uterus to achieve pregnancy. Despite its potential, IVF success varies, necessitating multiple cycles and posing risks like ovarian hyper-stimulation syndrome (OHSS) and ectopic pregnancy. High drug use can also lead to premature birth and low birth weight. Additionally, IVF raises ethical concerns regarding embryo selection. This study focuses on optimizing IVF treatment protocols using machine learning techniques. Leveraging AMH levels, age, BMI, and historical IVF cycle data, the study employs Q-Learning and Dynamic Programming. Q-Learning iteratively learns optimal treatment decisions in discrete state-action spaces, while Dynamic Programming enhances decision-making efficiency by solving complex subproblems. By analyzing trends between patient-specific factors and treatment outcomes, this study aims to predict the feasibility of IVF treatments and estimate the number of cycles required for success. Such insights aim to personalize IVF protocols, improving treatment efficacy and patient outcomes.