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Using a Decision Tree Algorithm Predictive Model for Sperm Count Assessment and Risk Factors in Health Screening

Hung-Hsiang Huang1, Chi-Jie Lu2,3,4, Mao-Jhen Jhou4

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

Risk Management and Healthcare Policy
|November 29, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning identified key factors impacting male fertility, including BMI and sleep quality, to predict sperm count. This aids healthcare professionals in assessing male infertility risks.

Keywords:
decision treefood metabolitemetabolic syndromesleep timesperm count

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Area of Science:

  • Reproductive Biology
  • Bioinformatics
  • Public Health

Background:

  • Infertility affects approximately 20% of couples globally.
  • Sperm quality is a critical determinant of successful conception and artificial reproduction outcomes.
  • Previous research utilized machine learning to identify ten risk factors for low sperm count in Taiwanese males.

Purpose of the Study:

  • To develop a predictive model for healthy sperm counts using machine learning.
  • To identify and analyze key risk factors influencing male fertility.
  • To provide healthcare professionals with a tool for assessing male infertility.

Main Methods:

  • Employed the Classification and Regression Trees (CART) algorithm to construct decision trees.
  • Utilized ten identified risk factors from previous machine learning analysis.
  • Evaluated decision tree performance using SMAPE, RAE, RRSE, and RMSE error metrics.

Main Results:

  • The top-performing decision tree incorporated BMI, uric acid (UA), sleep duration (ST), total cholesterol/HDL-C ratio, and blood urea nitrogen (BUN).
  • Confirmed the negative impact of metabolic syndrome indicators, such as high BMI, on sperm count.
  • Highlighted the positive association between adequate sleep and male fertility, and suggested UA and T-Cho/HDL-C as novel factors.

Conclusions:

  • A machine learning-based predictive model was established to assess low sperm counts.
  • Identified risk factors provide targets for future research and potential interventions.
  • Further model refinement with additional data is recommended for enhanced accuracy.