ArticleHealthcare (Basel, Switzerland)2023
Unboxing Industry-Standard AI Models for Male Fertility Prediction with SHAP.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 25 citations in OpenAlex.
- Explainable Artificial Intelligence in Assisted Reproductive Technology: Bridging Prediction and Clinical Judgment.Biomedicines · 2026Review
- Machine Learning-Based Prediction of Institutional Delivery Dropout (IDD) Among Nigerian Women: An Exploratory Study Using SHAP Interpretability.Journal of epidemiology and global health · 2026Article
- A comparative study on advanced predictive modeling of thyroid cancer recurrence using multi algorithmic machine learning frameworks.Scientific reports · 2025Article
- Visualized hysteroscopic artificial intelligence fertility assessment system for endometrial injury: an image-deep-learning study.Annals of medicine · 2025Article
- Enhancing the precision of male fertility diagnostics through bio inspired optimization techniques.Scientific reports · 2025Article
- Application of machine learning algorithms and SHAP explanations to predict fertility preference among reproductive women in Somalia.Scientific reports · 2025Article
- Artificial Intelligence Applications in Decision-Making for Disease Management:Sultan Qaboos University medical journal · 2025Article
- AI predictive models and advancements in microdissection testicular sperm extraction for non-obstructive azoospermia: a systematic scoping review.Human reproduction open · 2025Article
- Intelligent alert system for predicting invasive mechanical ventilation needs via noninvasive parameters: employing an integrated machine learning method with integration of multicenter databases.Medical & biological engineering & computing · 2024Article
- Artificial intelligence in andrology - fact or fiction: essential takeaway for busy clinicians.Asian journal of andrology · 2024Review
- The prediction of semen quality based on lifestyle behaviours by the machine learning based models.Reproductive biology and endocrinology : RB&E · 2024Article
- Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature.Healthcare (Basel, Switzerland) · 2024Review
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Infertility is a social stigma for individuals, and male factors cause approximately 30% of infertility. Despite this, male infertility is underrecognized and underrepresented as a disease. According to the World Health Organization (WHO), changes in lifestyle and environmental factors are the prime reasons for the declining rate of male fertility. Artificial intelligence (AI)/machine learning (ML) models have become an effective solution for early fertility detection. Seven industry-standard ML models are used: support vector machine, random forest (RF), decision tree, logistic regression, naïve bayes, adaboost, and multi-layer perception to detect male fertility. Shapley additive explanations (SHAP) are vital tools that examine the feature's impact on each model's decision making. On these, we perform a comprehensive comparative study to identify good and poor classification models. While dealing with the all-above-mentioned models, the RF model achieves an optimal accuracy and area under curve (AUC) of 90.47% and 99.98%, respectively, by considering five-fold cross-validation (CV) with the balanced dataset. Furthermore, we provide the SHAP explanations of existing models that attain good and poor performance. The findings of this study show that decision making (based on ML models) with SHAP provides thorough explanations for detecting male fertility, as well as a reference for clinicians for further treatment planning.
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