使用决策树算法预测模型对精子数量评估和健康查人口中的风险因素进行预测
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
概括
机器学习确定了影响男性生育能力的关键因素,包括BMI和睡眠质量,以预测精子数量. 这有助于医疗保健专业人员评估男性不育风险.
科学领域:
- 生殖生物学 生殖生物学
- 生物信息学是一种生物信息学.
- 公共卫生 公共卫生
背景情况:
- 在全球范围内,不孕症影响约20%的夫妇.
- 精子质量是成功受孕和人工生殖结果的关键决定因素.
- 之前的研究利用机器学习来确定台湾男性精子数量低的十个风险因素.
研究的目的:
- 使用机器学习开发健康精子计数的预测模型.
- 识别和分析影响男性生育能力的关键风险因素.
- 为医疗保健专业人员提供评估男性不孕症的工具.
主要方法:
- 使用分类和回归树 (CART) 算法构建决策树.
- 从之前的机器学习分析中利用了十个已识别的风险因素.
- 使用SMAPE,RAE,RRSE和RMSE错误指标评估决策树的性能.
主要成果:
- 性能最好的决策树包括BMI,尿酸 (UA),睡眠时间 (ST),总胆固醇/HDL-C比率和血液尿素 (BUN).
- 证实了代谢综合征指标 (如高BMI) 对精子数的负面影响.
- 强调了充足的睡眠与男性生育能力之间的积极关联,并建议UA和T-Cho/HDL-C作为新的因素.
结论:
- 建立了一个基于机器学习的预测模型来评估低精子数量.
- 鉴定的风险因素为未来的研究和潜在干预提供了目标.
- 为了提高准确性,建议进一步改进模型并添加额外的数据.
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