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Machine Learning Predictive Models for Evaluating Risk Factors Affecting Sperm Count: Predictions Based on Health

Hung-Hsiang Huang1, Shang-Ju Hsieh1, Ming-Shu Chen2

  • 1Division of Urology, Department of Surgery, Far Eastern Memorial Hospital, New Taipei City 220, Taiwan.

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|February 11, 2023
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Summary

Declining fertility rates are a concern. This study used machine learning to identify key risk factors for male sperm count, finding sleep time, body fat, blood pressure, and novel factors like alpha-fetoprotein and blood urea nitrogen are significant.

Keywords:
health screening indicatormachine learningmale reproductive healthsleep timesperm quality

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Area of Science:

  • Reproductive Health
  • Biostatistics
  • Machine Learning

Background:

  • Global decline in fertility rates, particularly in developed nations.
  • Taiwan faces a record low fertility rate, prompting government interventions.
  • Older age at marriage is linked to declining physical status and potential impacts on gamete quality.

Purpose of the Study:

  • To identify major risk factors affecting male sperm count using machine learning.
  • To analyze health screening data from a general male population in Taiwan.
  • To explore novel risk factors beyond traditional associations with male fertility.

Main Methods:

  • Utilized five machine learning algorithms: random forest, stochastic gradient boosting, least absolute shrinkage and selection operator regression, ridge regression, and extreme gradient boosting.
  • Analyzed annual health screening data of 1375 males from 2010 to 2017.
  • Employed metrics such as symmetric mean absolute percentage error and root mean squared error for performance evaluation.

Main Results:

  • Identified sleep time (ST), alpha-fetoprotein (AFP), body fat (BF), systolic blood pressure (SBP), and blood urea nitrogen (BUN) as top five risk factors for sperm count.
  • Confirmed negative impacts of metabolic syndrome indicators (BF, SBP) on sperm count.
  • Highlighted alpha-fetoprotein (AFP) and blood urea nitrogen (BUN) as novel risk factors for male fertility.

Conclusions:

  • Machine learning models effectively identify complex relationships influencing sperm count.
  • Findings support the role of metabolic syndrome and sleep duration in male reproductive health.
  • Identified novel risk factors (AFP, BUN) provide new avenues for research and public health strategies to address declining fertility rates.