Predicting Male Fertility Using Machine Learning: A Semen Parameters Based Analysis with the VISEM Dataset

• URL: http://arxiv.org/abs/2607.08429v1
• Date: Thu, 09 Jul 2026 12:51:46 GMT
• Title: Predicting Male Fertility Using Machine Learning: A Semen Parameters Based Analysis with the VISEM Dataset
• Abstract summary: This study investigates the use of machine learning algorithms to classify male fertility status based on key semen parameters.<n>This dataset includes semen samples from 85 participants, classified into three categories, i.e., Fertile, Sub-Fertile, and Infertile.<n>Among the more than 40 algorithms tested, the Nearestid Centro achieved an accuracy of 94.2%, outperforming other models.
• Score: 0.25128687379089687
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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