A Predictive Model for the Risk of Infertility in Men Using Machine Learning Algorithms

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Koc S., Tomak L., KARABULUT E.

JOURNAL OF UROLOGICAL SURGERY, vol.9, no.4, pp.265-271, 2022 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 9 Issue: 4
  • Publication Date: 2022
  • Doi Number: 10.4274/jus.galenos.2022.2021.0134
  • Journal Indexes: Emerging Sources Citation Index (ESCI), TR DİZİN (ULAKBİM)
  • Page Numbers: pp.265-271
  • Keywords: Classification, superlearner, prediction model, infertility, genetic factors
  • Ondokuz Mayıs University Affiliated: Yes


Objective: Infertility is a worldwide problem and causes considerable social, emotional and psychological stress between couples and among families. This study is aimed at determining the machine learning classifier capable of developing the most effective predictive model to determine the risk of infertility in men by genetic and external factors.Materials and Methods: The dataset was collected at Ondokuz Mayis University in the Department of Urology. The model was developed using supervised learning methods and by algorithms like decision tree, K nearest neighbor, Naive bayes, support vector machines, random forest and superlearner. Performances of the classifiers were assessed with the area under the curve.Results: Results of the performance evaluation showed that support vector machines and superlearner algorithms had area under curve of 96% and 97% respectively and this performance outperformed the remaining classifier. According to the results for importance of variables sperm concentration, follicular stimulating hormone and luteinizing hormone and some genetic factors are the important risk factors for infertility.Conclusion: These findings, whenever applied to any patient's record of infertility risk factors, can be used to predict the risk of infertility in men. The predictive model developed can be integrated into existing health information systems which can be used by urologists to predict patients' risk of infertility in real time.