🤖 AI Summary
Male infertility is often underdiagnosed due to the lack of objective assessment tools. This study systematically evaluates the performance of over forty machine learning models in classifying fertility status into three categories—fertile, subfertile, and infertile—using semen parameters (concentration, motility, and morphology) from the VISEM dataset comprising 85 subjects. Leveraging feature engineering, the LazyPredict automated modeling framework, five-fold cross-validation, and multiclass ROC-AUC analysis, the Nearest Centroid classifier emerged as the top-performing model, achieving an accuracy of 94.2%. Its performance significantly surpassed that of support vector machines and quadratic discriminant analysis, demonstrating strong potential as a clinical decision-support tool for male infertility diagnosis.
📝 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.