Male infertility is a significant yet often underdiagnosed aspect of reproductive health, with semen analysis serving as the cornerstone of clinical evaluation. This study investigates the use of machine learning algorithms to classify male fertility status based on key semen parameters, such as sperm concentration, motility, and morphology, using the VISEM dataset.
This dataset includes semen samples from 85 participants, classified into three categories: Fertile, Sub-Fertile, and Infertile, according to WHO criteria. After preprocessing and feature engineering, multiple classification models were trained and assessed using the LazyPredict framework.
Among the over 40 algorithms tested, the Nearest Centroid classifier achieved an accuracy of 94.2%, outperforming models like Support Vector Machines and Quadratic Discriminant Analysis. The model's robustness was validated using 5-fold cross-validation and multiclass ROC-AUC analysis.
This study illustrates that machine learning models can provide fast, accurate, and objective assessments of semen quality, potentially supporting clinical decision-making in andrology and assisted reproductive technologies. These findings emphasize the growing potential of machine learning to enhance fertility diagnostics and inform patient-specific treatment strategies.
Blogger's Review: This study highlights the immense potential of machine learning in fertility prediction, particularly in clinical decision support. With precise model evaluations, more personalized treatment plans can be provided for infertile patients, significantly overcoming the limitations of traditional methods, which warrants further exploration and promotion.