Nearest Centroid classifier achieves 94.2% accuracy on VISEM semen dataset

A study utilized the VISEM dataset, comprising semen samples from 85 participants classified as Fertile, Sub-Fertile, or Infertile, to evaluate machine learning algorithms for predicting male fertility status based on sperm concentration, motility, and morphology.

• The Nearest Centroid classifier achieved an accuracy of 94.2%, outperforming Support Vector Machines and Quadratic Discriminant Analysis among over 40 tested algorithms.
• Model robustness was validated using 5-fold cross-validation and multiclass ROC-AUC analysis.
• The research demonstrates that machine learning can provide fast, accurate, and objective assessments of semen quality to support clinical decision-making in andrology.