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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...

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Autores principales: GhoshRoy, Debasmita, Alvi, Parvez Ahmad, Santosh, KC
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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.
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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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