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

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Názov: Using a Decision Tree Algorithm Predictive Model for Sperm Count Assessment and Risk Factors in Health Screening Population.
Autori: Huang, Hung-Hsiang, Lu, Chi-Jie, Jhou, Mao-Jhen, Liu, Tzu-Chi, Yang, Chih-Te, Hsieh, Shang-Ju, Yang, Wen-Jen, Chang, Hsiao-Chun, Chen, Ming-Shu
Zdroj: Risk Management & Healthcare Policy; Nov2023, Vol. 16, p2469-2478, 10p
Predmety: HEALTH risk assessment, DECISION trees, SPERM count, INFERTILITY, CART algorithms, MACHINE learning
Abstrakt: 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. [ABSTRACT FROM AUTHOR]
Copyright of Risk Management & Healthcare Policy is the property of Dove Medical Press Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Using a Decision Tree Algorithm Predictive Model for Sperm Count Assessment and Risk Factors in Health Screening Population.
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  Data: <searchLink fieldCode="AR" term="%22Huang%2C+Hung-Hsiang%22">Huang, Hung-Hsiang</searchLink><br /><searchLink fieldCode="AR" term="%22Lu%2C+Chi-Jie%22">Lu, Chi-Jie</searchLink><br /><searchLink fieldCode="AR" term="%22Jhou%2C+Mao-Jhen%22">Jhou, Mao-Jhen</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Tzu-Chi%22">Liu, Tzu-Chi</searchLink><br /><searchLink fieldCode="AR" term="%22Yang%2C+Chih-Te%22">Yang, Chih-Te</searchLink><br /><searchLink fieldCode="AR" term="%22Hsieh%2C+Shang-Ju%22">Hsieh, Shang-Ju</searchLink><br /><searchLink fieldCode="AR" term="%22Yang%2C+Wen-Jen%22">Yang, Wen-Jen</searchLink><br /><searchLink fieldCode="AR" term="%22Chang%2C+Hsiao-Chun%22">Chang, Hsiao-Chun</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Ming-Shu%22">Chen, Ming-Shu</searchLink>
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  Data: Risk Management & Healthcare Policy; Nov2023, Vol. 16, p2469-2478, 10p
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  Data: <searchLink fieldCode="DE" term="%22HEALTH+risk+assessment%22">HEALTH risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22DECISION+trees%22">DECISION trees</searchLink><br /><searchLink fieldCode="DE" term="%22SPERM+count%22">SPERM count</searchLink><br /><searchLink fieldCode="DE" term="%22INFERTILITY%22">INFERTILITY</searchLink><br /><searchLink fieldCode="DE" term="%22CART+algorithms%22">CART algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Risk Management & Healthcare Policy is the property of Dove Medical Press Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.2147/RMHP.S433193
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        Text: English
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        Type: general
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      – SubjectFull: SPERM count
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      – SubjectFull: MACHINE learning
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              Text: Nov2023
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