IVF Success Prediction using Machine Learning Techniques: A Comparative Study
Keywords:IVF, Machine Learning, Random Forest, Prediction, Assisted Reproductive Technology
IVF is a popularly used assisted reproductive therapy that aids couples who are having trouble getting pregnant naturally. Medical providers must be able to predict an IVF cycle's effectiveness to tailor their care and enhance outcomes. To forecast the likelihood of a successful IVF cycle, this study proposes a machine learning (ML) model based on the random forest approach.
A dataset of patient cycles was collected from the author of a research paper we came across while studying the topic. The dataset includes patient demographic and clinical variables, such as age, body mass index (BMI), semen test results, number of retrieved oocytes, and female and male infertility factors. After preprocessing and feature engineering, we used the random forest, support vector machine, gradient boosting, and logistic regression algorithms to build four classification models to predict IVF success and compared their results. The Gradient Boosting algorithm showed the highest accuracy of 87%, whereas the SVM model showed the least accuracy with 67%. The most important features for the prediction were age, number of retrieved oocytes, and embryo quality, consistent with previous studies. The research shows the potential of machine learning models for predicting IVF success, which can assist physicians in making wise choices and enhancing the results for patients. To confirm the generalizability of the approach, additional studies with larger datasets and more varied patient populations are required.
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