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This study employs machine learning algorithms to classify male fertility status based on semen parameters, utilizing the VISEM dataset, which includes samples from 85 participants categorized into Fertile, Sub-Fertile, and Infertile groups. After extensive pre-processing and feature engineering, the Nearest Centroid classifier achieved a remarkable accuracy of 94.2%, surpassing traditional models like Support Vector Machines and Quadratic Discriminant Analysis. The findings highlight the potential of machine learning to deliver rapid and precise assessments of semen quality, thereby enhancing clinical decision-making in reproductive health.
Machine learning can classify male fertility with over 94% accuracy, offering a game-changing tool for reproductive health diagnostics.
Male infertility is a significant yet often underdiagnosed aspect of reproductive health, with semen analysis serving as the cornerstone of clinical evaluation. To address this problem, this study investigates the use of machine learning algorithms to classify male fertility status based on key semen parameters, i.e., sperm concentration, motility, and morphology, using the VISEM dataset. This dataset includes semen samples from 85 participants, classified into three categories, i.e., Fertile, Sub-Fertile, and Infertile, according to the World Health Organization's criteria. After pre-processing and feature engineering, the dataset was used to train and assess multiple classification models using the LazyPredict framework. Among the more than 40 algorithms tested, the Nearest Centroid classifier achieved an accuracy of 94.2%, outperforming other models such as 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.
