Predicting Male Fertility Using Machine Learning: A Semen Parameters Based Analysis with the VISEM Dataset
arXiv:2607.08429v1 Announce Type: new 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 He
The increasing availability of reproductive health datasets and advancements in machine learning algorithms are enabling more sophisticated analyses of complex biological factors like fertility.
This development allows for earlier and more accurate identification of male fertility issues, potentially improving intervention strategies and impacting societal demographic trends.
The diagnostic process for male infertility could become more efficient and accessible, moving beyond traditional subjective assessments to data-driven predictions.
- · Reproductive health clinics
- · Couples struggling with infertility
- · AI healthcare developers
- · Demographic researchers
- · Traditional diagnostic labs
Improved early detection and personalized treatment plans for male infertility.
Potential for a slight but measurable positive impact on fertility rates in developed countries grappling with demographic decline.
The integration of AI into reproductive health could lead to ethical debates regarding predictive diagnostics and genetic screening.
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