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

doi:10.3390/jcm12031220

淡江大學機構典藏 > 商管學院 > 企業管理學系暨研究所 > 期刊論文 > Item 987654321/123216

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/123216

Title:	Machine Learning Predictive Models for Evaluating Risk Factors Affecting Sperm Count: Predictions Based on Health Screening Indicators
Authors:	Huang, Hung-Hsiang;Hsieh, Shang-Ju;Chen, Ming-Shu;Jhou, Mao-Jhen;Liu, Tzu-Chi;Shen, Hsiang-Li;Yang, Chih-Te;Hung, Chung-Chih;Yu, Ya-Yen;Lu*, Chi-Jie
Keywords:	health screening indicator;machine learning;male reproductive health;sperm quality;sleep time
Date:	2023-02-03
Issue Date:	2023-04-28 17:19:13 (UTC+8)
Publisher:	MDPI AG
Abstract:	In many countries, especially developed nations, the fertility rate and birth rate have continually declined. Taiwan’s fertility rate has paralleled this trend and reached its nadir in 2022. Therefore, the government uses many strategies to encourage more married couples to have children. However, couples marrying at an older age may have declining physical status, as well as hypertension and other metabolic syndrome symptoms, in addition to possibly being overweight, which have been the focus of the studies for their influences on male and female gamete quality. Many previous studies based on infertile people are not truly representative of the general population. This study proposed a framework using five machine learning (ML) predictive algorithms—random forest, stochastic gradient boosting, least absolute shrinkage and selection operator regression, ridge regression, and extreme gradient boosting—to identify the major risk factors affecting male sperm count based on a major health screening database in Taiwan. Unlike traditional multiple linear regression, ML algorithms do not need statistical assumptions and can capture non-linear relationships or complex interactions between dependent and independent variables to generate promising performance. We analyzed annual health screening data of 1375 males from 2010 to 2017, including data on health screening indicators, sourced from the MJ Group, a major health screening center in Taiwan. The symmetric mean absolute percentage error, relative absolute error, root relative squared error, and root mean squared error were used as performance evaluation metrics. Our results show that sleep time (ST), alpha-fetoprotein (AFP), body fat (BF), systolic blood pressure (SBP), and blood urea nitrogen (BUN) are the top five risk factors associated with sperm count. ST is a known risk factor influencing reproductive hormone balance, which can affect spermatogenesis and final sperm count. BF and SBP are risk factors associated with metabolic syndrome, another known risk factor of altered male reproductive hormone systems. However, AFP has not been the focus of previous studies on male fertility or semen quality. BUN, the index for kidney function, is also identified as a risk factor by our established ML model. Our results support previous findings that metabolic syndrome has negative impacts on sperm count and semen quality. Sleep duration also has an impact on sperm generation in the testes. AFP and BUN are two novel risk factors linked to sperm counts. These findings could help healthcare personnel and law makers create strategies for creating environments to increase the country’s fertility rate. This study should also be of value to follow-up research.
Relation:	Journal of Clinical Medicine 12(3), 1220
DOI:	10.3390/jcm12031220
Appears in Collections:	[企業管理學系暨研究所] 期刊論文

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