The prediction of semen quality based on lifestyle behaviours by the machine learning based models.

IF 4.2 2区医学 Q1 ENDOCRINOLOGY & METABOLISM

Reproductive Biology and Endocrinology Pub Date : 2024-08-29 DOI:10.1186/s12958-024-01268-w

Aykut Aykaç, Coşkun Kaya, Özer Çelik, Mehmet Erhan Aydın, Mustafa Sungur

{"title":"The prediction of semen quality based on lifestyle behaviours by the machine learning based models.","authors":"Aykut Aykaç, Coşkun Kaya, Özer Çelik, Mehmet Erhan Aydın, Mustafa Sungur","doi":"10.1186/s12958-024-01268-w","DOIUrl":null,"url":null,"abstract":"Purpose: To find the machine learning (ML) method that has the highest accuracy in predicting the semen quality of men based on basic questionnaire data about lifestyle behavior.Methods: The medical records of men whose semen was analyzed for any reason were collected. Those who had data about their lifestyle behaviors were included in the study. All semen analyses of the men included were evaluated according to the WHO 2021 guideline. All semen analyses were categorized as normozoospermia, oligozoospermia, teratozoospermia, and asthenozoospermia. The Extra Trees Classifier, Average (AVG) Blender, Light Gradient Boosting Machine (LGBM) Classifier, eXtreme Gradient Boosting (XGB) Classifier, Logistic Regression, and Random Forest Classifier techniques were used as ML algorithms.Results: Seven hundred thirty-four men who met the inclusion criteria and had data about lifestyle behavior were included in the study. 356 men (48.5%) had abnormal semen results, 204 (27.7%) showed the presence of oligozoospermia, 193 (26.2%) asthenozoospermia, and 265 (36.1%) teratozoospermia according to the WHO 2021. The AVG Blender model had the highest accuracy and AUC for predicting normozoospermia and teratozoospermia. The Extra Trees Classifier and Random Forest Classifier models achieved the best performance for predicting oligozoospermia and asthenozoospermia, respectively.Conclusion: The ML models have the potential to predict semen quality based on lifestyles.","PeriodicalId":21011,"journal":{"name":"Reproductive Biology and Endocrinology","volume":"22 1","pages":"112"},"PeriodicalIF":4.2000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11360792/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reproductive Biology and Endocrinology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12958-024-01268-w","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}

引用次数: 0

Abstract

Purpose: To find the machine learning (ML) method that has the highest accuracy in predicting the semen quality of men based on basic questionnaire data about lifestyle behavior.

Methods: The medical records of men whose semen was analyzed for any reason were collected. Those who had data about their lifestyle behaviors were included in the study. All semen analyses of the men included were evaluated according to the WHO 2021 guideline. All semen analyses were categorized as normozoospermia, oligozoospermia, teratozoospermia, and asthenozoospermia. The Extra Trees Classifier, Average (AVG) Blender, Light Gradient Boosting Machine (LGBM) Classifier, eXtreme Gradient Boosting (XGB) Classifier, Logistic Regression, and Random Forest Classifier techniques were used as ML algorithms.

Results: Seven hundred thirty-four men who met the inclusion criteria and had data about lifestyle behavior were included in the study. 356 men (48.5%) had abnormal semen results, 204 (27.7%) showed the presence of oligozoospermia, 193 (26.2%) asthenozoospermia, and 265 (36.1%) teratozoospermia according to the WHO 2021. The AVG Blender model had the highest accuracy and AUC for predicting normozoospermia and teratozoospermia. The Extra Trees Classifier and Random Forest Classifier models achieved the best performance for predicting oligozoospermia and asthenozoospermia, respectively.

Conclusion: The ML models have the potential to predict semen quality based on lifestyles.

查看原文本刊更多论文

通过基于机器学习的模型，根据生活行为预测精液质量。

目的：根据有关生活方式的基本问卷数据，找出预测男性精液质量准确率最高的机器学习（ML）方法：方法：收集因任何原因对精液进行分析的男性的医疗记录。方法：收集因任何原因进行精液分析的男性的医疗记录，并将有生活方式行为数据的男性纳入研究范围。所有男性精液分析结果均根据世界卫生组织 2021 年指南进行评估。所有精液分析结果都被分为正常精子症、少精子症、畸形精子症和无精子症。采用额外树分类器、平均（AVG）混合器、轻梯度提升机（LGBM）分类器、极端梯度提升（XGB）分类器、逻辑回归和随机森林分类器技术作为多重L算法：研究共纳入了 734 名符合纳入标准并拥有生活方式数据的男性。根据 WHO 2021 标准，356 名男性（48.5%）精液结果异常，其中 204 名（27.7%）显示存在少精子症，193 名（26.2%）显示存在无精子症，265 名（36.1%）显示存在畸形精子症。AVG Blender 模型预测正常无精子症和畸形无精子症的准确率和 AUC 最高。Extra Trees 分类器和随机森林分类器模型分别在预测少精症和无精症方面表现最佳：结论：ML 模型具有根据生活方式预测精液质量的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Reproductive Biology and Endocrinology 医学-内分泌学与代谢

CiteScore

7.90

自引率

2.30%

发文量

161

审稿时长

4-8 weeks

期刊介绍： Reproductive Biology and Endocrinology publishes and disseminates high-quality results from excellent research in the reproductive sciences. The journal publishes on topics covering gametogenesis, fertilization, early embryonic development, embryo-uterus interaction, reproductive development, pregnancy, uterine biology, endocrinology of reproduction, control of reproduction, reproductive immunology, neuroendocrinology, and veterinary and human reproductive medicine, including all vertebrate species.