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Unboxing Industry-Standard AI Models for Male Fertility Prediction with SHAP
Infertility is a social stigma for individuals, and male factors cause approximately 30% of infertility. Despite this, male infertility is underrecognized and underrepresented as a disease. According to the World Health Organization (WHO), changes in lifestyle and environmental factors are the prime...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10094449/ https://www.ncbi.nlm.nih.gov/pubmed/37046855 http://dx.doi.org/10.3390/healthcare11070929 |
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author | GhoshRoy, Debasmita Alvi, Parvez Ahmad Santosh, KC |
author_facet | GhoshRoy, Debasmita Alvi, Parvez Ahmad Santosh, KC |
author_sort | GhoshRoy, Debasmita |
collection | PubMed |
description | Infertility is a social stigma for individuals, and male factors cause approximately 30% of infertility. Despite this, male infertility is underrecognized and underrepresented as a disease. According to the World Health Organization (WHO), changes in lifestyle and environmental factors are the prime reasons for the declining rate of male fertility. Artificial intelligence (AI)/machine learning (ML) models have become an effective solution for early fertility detection. Seven industry-standard ML models are used: support vector machine, random forest (RF), decision tree, logistic regression, naïve bayes, adaboost, and multi-layer perception to detect male fertility. Shapley additive explanations (SHAP) are vital tools that examine the feature’s impact on each model’s decision making. On these, we perform a comprehensive comparative study to identify good and poor classification models. While dealing with the all-above-mentioned models, the RF model achieves an optimal accuracy and area under curve (AUC) of 90.47% and 99.98%, respectively, by considering five-fold cross-validation (CV) with the balanced dataset. Furthermore, we provide the SHAP explanations of existing models that attain good and poor performance. The findings of this study show that decision making (based on ML models) with SHAP provides thorough explanations for detecting male fertility, as well as a reference for clinicians for further treatment planning. |
format | Online Article Text |
id | pubmed-10094449 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100944492023-04-13 Unboxing Industry-Standard AI Models for Male Fertility Prediction with SHAP GhoshRoy, Debasmita Alvi, Parvez Ahmad Santosh, KC Healthcare (Basel) Article Infertility is a social stigma for individuals, and male factors cause approximately 30% of infertility. Despite this, male infertility is underrecognized and underrepresented as a disease. According to the World Health Organization (WHO), changes in lifestyle and environmental factors are the prime reasons for the declining rate of male fertility. Artificial intelligence (AI)/machine learning (ML) models have become an effective solution for early fertility detection. Seven industry-standard ML models are used: support vector machine, random forest (RF), decision tree, logistic regression, naïve bayes, adaboost, and multi-layer perception to detect male fertility. Shapley additive explanations (SHAP) are vital tools that examine the feature’s impact on each model’s decision making. On these, we perform a comprehensive comparative study to identify good and poor classification models. While dealing with the all-above-mentioned models, the RF model achieves an optimal accuracy and area under curve (AUC) of 90.47% and 99.98%, respectively, by considering five-fold cross-validation (CV) with the balanced dataset. Furthermore, we provide the SHAP explanations of existing models that attain good and poor performance. The findings of this study show that decision making (based on ML models) with SHAP provides thorough explanations for detecting male fertility, as well as a reference for clinicians for further treatment planning. MDPI 2023-03-23 /pmc/articles/PMC10094449/ /pubmed/37046855 http://dx.doi.org/10.3390/healthcare11070929 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article GhoshRoy, Debasmita Alvi, Parvez Ahmad Santosh, KC Unboxing Industry-Standard AI Models for Male Fertility Prediction with SHAP |
title | Unboxing Industry-Standard AI Models for Male Fertility Prediction with SHAP |
title_full | Unboxing Industry-Standard AI Models for Male Fertility Prediction with SHAP |
title_fullStr | Unboxing Industry-Standard AI Models for Male Fertility Prediction with SHAP |
title_full_unstemmed | Unboxing Industry-Standard AI Models for Male Fertility Prediction with SHAP |
title_short | Unboxing Industry-Standard AI Models for Male Fertility Prediction with SHAP |
title_sort | unboxing industry-standard ai models for male fertility prediction with shap |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10094449/ https://www.ncbi.nlm.nih.gov/pubmed/37046855 http://dx.doi.org/10.3390/healthcare11070929 |
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