Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature
Male infertility is a relevant public health problem, but there is no systematic review of the different machine learning (ML) models and their accuracy so far. The present review aims to comprehensively investigate the use of ML algorithms in predicting male infertility, thus reporting the accuracy...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-04-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/12/7/781 |
| _version_ | 1797212585108439040 |
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| author | Vivian Schmeis Arroyo Marco Iosa Gabriella Antonucci Daniela De Bartolo |
| author_facet | Vivian Schmeis Arroyo Marco Iosa Gabriella Antonucci Daniela De Bartolo |
| author_sort | Vivian Schmeis Arroyo |
| collection | DOAJ |
| description | Male infertility is a relevant public health problem, but there is no systematic review of the different machine learning (ML) models and their accuracy so far. The present review aims to comprehensively investigate the use of ML algorithms in predicting male infertility, thus reporting the accuracy of the used models in the prediction of male infertility as a primary outcome. Particular attention will be paid to the use of artificial neural networks (ANNs). A comprehensive literature search was conducted in PubMed, Scopus, and Science Direct between 15 July and 23 October 2023, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We performed a quality assessment of the included studies using the recommended tools suggested for the type of study design adopted. We also made a screening of the Risk of Bias (RoB) associated with the included studies. Thus, 43 relevant publications were included in this review, for a total of 40 different ML models detected. The studies included reported a good quality, even if RoB was not always good for all the types of studies. The included studies reported a median accuracy of 88% in predicting male infertility using ML models. We found only seven studies using ANN models for male infertility prediction, reporting a median accuracy of 84%. |
| first_indexed | 2024-04-24T10:44:43Z |
| format | Article |
| id | doaj.art-9980a776ac63426082a29f4b3d4cf1ef |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-04-24T10:44:43Z |
| publishDate | 2024-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-9980a776ac63426082a29f4b3d4cf1ef2024-04-12T13:19:00ZengMDPI AGHealthcare2227-90322024-04-0112778110.3390/healthcare12070781Predicting Male Infertility Using Artificial Neural Networks: A Review of the LiteratureVivian Schmeis Arroyo0Marco Iosa1Gabriella Antonucci2Daniela De Bartolo3Department of Psychology, University Sapienza of Rome, 00185 Rome, ItalyDepartment of Psychology, University Sapienza of Rome, 00185 Rome, ItalyDepartment of Psychology, University Sapienza of Rome, 00185 Rome, ItalySanta Lucia Foundation, Scientific Institute for Research, Hospitalization and Health Care (IRCCS), 00179 Rome, ItalyMale infertility is a relevant public health problem, but there is no systematic review of the different machine learning (ML) models and their accuracy so far. The present review aims to comprehensively investigate the use of ML algorithms in predicting male infertility, thus reporting the accuracy of the used models in the prediction of male infertility as a primary outcome. Particular attention will be paid to the use of artificial neural networks (ANNs). A comprehensive literature search was conducted in PubMed, Scopus, and Science Direct between 15 July and 23 October 2023, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We performed a quality assessment of the included studies using the recommended tools suggested for the type of study design adopted. We also made a screening of the Risk of Bias (RoB) associated with the included studies. Thus, 43 relevant publications were included in this review, for a total of 40 different ML models detected. The studies included reported a good quality, even if RoB was not always good for all the types of studies. The included studies reported a median accuracy of 88% in predicting male infertility using ML models. We found only seven studies using ANN models for male infertility prediction, reporting a median accuracy of 84%.https://www.mdpi.com/2227-9032/12/7/781machine learningmale infertilityartificial intelligencestatistical models |
| spellingShingle | Vivian Schmeis Arroyo Marco Iosa Gabriella Antonucci Daniela De Bartolo Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature Healthcare machine learning male infertility artificial intelligence statistical models |
| title | Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature |
| title_full | Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature |
| title_fullStr | Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature |
| title_full_unstemmed | Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature |
| title_short | Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature |
| title_sort | predicting male infertility using artificial neural networks a review of the literature |
| topic | machine learning male infertility artificial intelligence statistical models |
| url | https://www.mdpi.com/2227-9032/12/7/781 |
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