To determine and predict the success rate of various treatments in primarily infertile patients with the help of machine learning.

Abstract

Background: Infertility, defined as the inability to conceive after 12 months of 

regular unprotected sexual intercourse, impacts millions of humans globally. This 

condition not only challenges public health systems but also significantly affects 

psychological and social well-being. Current therapeutic approaches vary in 

accessibility and effectiveness, necessitating advancements in diagnostic and 

treatment modalities. Machine learning (ML), a subset of artificial intelligence, 

offers potential benefits in refining diagnostic accuracies and enhancing the 

efficacy of infertility treatments through personalized care and predictive 

analytics.

Methods: This study utilized machine learning algorithms to predict the success 

rates of specific infertility treatments, including Intrauterine Insemination (IUI), 

In-Vitro Fertilization (IVF), and Intracytoplasmic Sperm Injection (ICSI), among 

a sample of primarily infertile patients. Retrospective and prospective data 

spanning six months from 567 couples treated at Sunflower Hospital and Gayatri 

Hospital were analyzed. ML models such as Decision Trees, Logistic Regression, 

and XGBoost were applied, focusing on an array of predictive features including 

patient age, infertility duration, previous medical and treatment histories, among 

others. These features were rigorously selected based on their importance scores 

derived from the ML algorithms to optimize the predictive performance.

Results and Discussion: The application of machine learning models 

demonstrated significant potential in predicting the outcomes of infertility 

treatments. The Decision Tree model exhibited a prediction accuracy of 85.54% 

for IUI success rates, which was notably higher than the other models tested. For 

IVF and ICSI treatments combined, the XGBoost model achieved an accuracy of 

72.09%, indicating robust model performance in these more complex cases. 

These models were effective in handling multifaceted data and yielded insights 

that could assist in making informed clinical decisions. Notably, the Logistic 

Regression model displayed substantial accuracy improvements over traditional 

statistical methods, particularly in reducing the likelihood of false negatives, 

thereby ensuring that fewer patients undergoing viable treatments were 

incorrectly predicted to have poor outcomes.

Conclusions: Integrating machine learning into reproductive healthcare can substantially improve the precision of infertility treatment protocols, predict 

treatment successes more accurately, and increase the overall efficacy of 

interventions. This study underscores the transformative potential of ML in 

healthcare, providing a framework for future applications in infertility treatment 

and beyond. Continued research and development into these models are 

recommended to confirm their effectiveness across different demographic and 

geographical contexts and to explore their utility in other medical domains.substantially improve the precision of infertility treatment protocols, predict 

treatment successes more accurately, and increase the overall efficacy of 

interventions. This study underscores the transformative potential of ML in 

healthcare, providing a framework for future applications in infertility treatment 

and beyond. Continued research and development into these models are 

recommended to confirm their effectiveness across different demographic and 

geographical contexts and to explore their utility in other medical domains.