Genetic algorithm–assisted machine learning for clinical pregnancy prediction in in vitro fertilization

BACKGROUND: A clinical pregnancy prediction model was developed by implementing machine learning technology that uses a combination of static images and medical data to calculate the outcome of an in vitro fertilization cycle. OBJECTIVE: To provide a system that can accurately and sufficiently assist with decision making that is critical to in vitro fertilization cycles, primarily embryo selection. STUDY DESIGN: Historical medical data, which consist of clinical information and a complete transferred embryo image dataset, of 697 patients who underwent unique in vitro fertilization were collected. Various techniques of machine learning were used, namely decision tree, random forest, and gradient boosting; each technique used the same data configuration for performance comparison and was subsequently optimized using genetic algorithm. RESULTS: A prediction model with a peak accuracy of approximately 65% was achieved. Significant differences in the performances of the 3 selected algorithms were apparent. Nonetheless, additional metric measurements, such as receiver operating characteristic, area under the receiver operating characteristic curve score, accuracy, and loss, suggested that the gradient boosting model performed the best in predicting clinical pregnancy. CONCLUSION: This study served as a stepping stone toward the application of in vitro fertilization prediction models that use machine learning techniques. However, additional validation steps are required to boost the model's performance for its implementation in the clinical setting.