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

doi:10.2147/RMHP.S433193

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 987654321/124820

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題名:	Using a Decision Tree Algorithm Predictive Model for Sperm Count Assessment and Risk Factors in Health Screening Population
作者:	Huang HH;Lu CJ;hou MJ;Liu TC;Yang CT;Hsieh SJ;Yang WJ;Chang HC;Chen MS
關鍵詞:	sperm count;metabolic syndrome;sleep time;food metabolite;decision tree
日期:	2023-11-15
上傳時間:	2023-12-13 12:05:41 (UTC+8)
出版者:	DOVE MEDICAL PRESS LTD
摘要:	Purpose Approximately 20% of couples face infertility challenges and struggle to conceive naturally. Despite advances in artificial reproduction, its success hinges on sperm quality. Our previous study used five machine learning (ML) algorithms, random forest, stochastic gradient boosting, least absolute shrinkage and selection operator regression, ridge regression, and extreme gradient boosting, to model health data from 1375 Taiwanese males and identified ten risk factors affecting sperm count. Methods We employed the CART algorithm to generate decision trees using identified risk factors to predict healthy sperm counts. Four error metrics, SMAPE, RAE, RRSE, and RMSE, were used to evaluate the decision trees. We identified the top five decision trees based on their low errors and discussed in detail the tree with the least error. Results The decision tree featuring the least error, comprising BMI, UA, ST, T-Cho/HDL-C ratio, and BUN, corroborated the negative impacts of metabolic syndrome, particularly high BMI, on sperm count, while emphasizing the link between good sleep and male fertility. Our study also sheds light on the potentially significant influence of high BUN on spermatogenesis. Two novel risk factors, T-Cho/HDL-C and UA, warrant further investigation. Conclusion The ML algorithm established a predictive model for healthcare personnel to assess low sperm counts. Refinement of the model using additional data is crucial for improved precision. The risk factors identified offer avenues for future investigations.
關聯:	Risk Management and Healthcare Policy 2023 16,　2469-2478
DOI:	10.2147/RMHP.S433193
顯示於類別:	[企業管理學系暨研究所] 期刊論文

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