Enhancing In-Vitro Fertilization Outcomes through Machine Learning: A Comparative Analysis of Predictive Models and Dataset Evaluation

By enabling the fertilisation of eggs and sperm outside the body, in-vitro fertilisation (IVF) offers couples struggling with infertility hope through a complex medical procedure. The complex procedure involves causing the ovaries to release eggs, extracting the eggs, fertilising them with sperm in a lab environment to create embryos, and then putting the embryos into the uterus.Machine learning methodologies such as K-nearest neighbors (KNN), random forest, and support vector machine (SVM) exhibit potential in forecasting IVF outcomes and alleviating the physical and emotional burden associated with treatment. This study's objective was to examine several machine learning algorithms and assess the IVF dataset's reliability by contrasting it with other datasets. Consequently, the KNN model achieved a 64% accuracy rate, whereas the SVM, random forest, and logistic regression models obtained perfect accuracy rates of 100%. Assessing the IVF dataset using standardized data models through benchmarking helps confirm its quality, relevance, and importance, thus guiding efforts to improve IVF success rates. In essence, machine learning models are quite good at predicting the outcomes of IVF, which leads to customised reproductive therapies that improve IVF success.