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

Jul 16, 2026

Male infertility represents a major reproductive health challenge that is often underdiagnosed, and semen analysis remains the clinical reference for evaluating male fertility. This study applies supervised machine learning to classify male fertility status from semen parameters—sperm concentration, motility, and morphology—using the VISEM dataset. The dataset contains semen samples from 85 participants labeled into Fertile, Sub-Fertile, and Infertile classes based on WHO criteria. After preprocessing and feature engineering, over 40 models are evaluated with LazyPredict, achieving 94.2% accuracy via Nearest Centroid, validated through 5-fold cross-validation and multiclass ROC-AUC analysis, supporting objective decision support for andrology and ART.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages22

Reading Time55 min

Abstract

Introduction

Background: infertility and semen analysis

Motivation: AI/ML for fertility diagnostics

Study objective and dataset

Method overview and evaluation approach

arXiv :2607 .08429v 1 [ cs .LG] 9 Jul 2026 Predicting Male Fertility Using Machine Learning: A Semen Parameters Based Analysis with the VISEM Dataset Shahnawaz Qureshi 1 , Raja Khurram Shahzad2*, Muhammad Fozan 1 , Emal Kawal 1 , Syed Aziz Shah3 , Sattam Al-Anazi4 , Syed Muhammad Zeeshan Iqbal5 1 School of Computing, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Haripur, Khyber Pakhtunkhwa, Pakistan. 2 Department of Communication, Quality Management and Information Systems, Mid Sweden University, ¨Ostersund Campus, Sweden. 3 Healthcare Sensing Technology, Center for Intelligent Healthcare, Coventry University, United Kingdom. 4 E-Serivces Department, Saudi Standards, Metrology and Quality Organization, Riyadh, Saudi Arabia. 5 Research and Development, Brightware LLC, Riyadh, Saudi Arabia. *Corresponding author(s). E-mail(s): [raja-khurram.shahzad@miun.se](raja-khurram.shahzad@miun.se) ; Contributing authors: [shahnawaz.qureshi@paf-iast.edu.pk](shahnawaz.qureshi@paf-iast.edu.pk) ; [mfozan67@gmail.com](mfozan67@gmail.com) ; [b22f1813cs118@fecid.paf-iast.edu.pk](b22f1813cs118@fecid.paf-iast.edu.pk) ; [syed.shah@coventry.ac.uk](syed.shah@coventry.ac.uk) ; [s.homidi@saso.gov.sa](s.homidi@saso.gov.sa) ; [mzeeshan01@gmail.com](mzeeshan01@gmail.com) ; 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 1 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. Keywords: Male infertility, Semen analysis, Sperm motility, Sperm morphology, Machine learning, Fertility classification, VISEM dataset, Artificial intelligence in reproduction, Reproductive health, Clinical decision support, Reproductive diagnostics 1 Introduction Infertility is defined as the inability to conceive after 12 months of unprotected intercourse and affects approximately 17 .5% of the global adult population. Male factors are responsible for nearly half of all infertility cases, occurring either independently (up to 20%) or in combination with female infertility (up to 40%) Vander Borght and Wyns (2018) . Semen analysis is the primary clinical tool for assessing male reproductive health. It is used to evaluate critical parameters, such as sperm concentration, motility, and morphology, according to guidelines established by the World Health Organization (WHO) Kumar and Singh (2015), as illustrated in the Figure 1 . However, despite its clinical significance, traditional semen evaluation is performed manually and is subject to observer bias, inter-laboratory variability, and inconsistent interpretation. These factors may result in inaccurate or delayed diagnoses Leslie et al. (2024) . As fertility evaluations become in

FAQs

Which machine learning model performed best and how was it validated?

The Nearest Centroid classifier achieved 94.2% accuracy, validated using 5-fold cross-validation and multiclass ROC-AUC analysis.

Jul 16, 2026

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