Explainable AI to Predict Male Fertility Using Extreme Gradient Boosting Algorithm with SMOTE
Infertility is a common problem across the world. Infertility distribution due to male factors ranges from 40% to 50%. Existing artificial intelligence (AI) systems are not often human interpretable. Further, clinicians are unaware of how data analytical tools make decisions, and as a result, they h...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/1/15 |
| _version_ | 1797626045425254400 |
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| author | Debasmita GhoshRoy Parvez Ahmad Alvi KC Santosh |
| author_facet | Debasmita GhoshRoy Parvez Ahmad Alvi KC Santosh |
| author_sort | Debasmita GhoshRoy |
| collection | DOAJ |
| description | Infertility is a common problem across the world. Infertility distribution due to male factors ranges from 40% to 50%. Existing artificial intelligence (AI) systems are not often human interpretable. Further, clinicians are unaware of how data analytical tools make decisions, and as a result, they have limited exposure to healthcare. Using explainable AI tools makes AI systems transparent and traceable, enhancing users’ trust and confidence in decision-making. The main contribution of this study is to introduce an explainable model for investigating male fertility prediction. Nine features related to lifestyle and environmental factors are utilized to develop a male fertility prediction model. Five AI tools, namely support vector machine, adaptive boosting, conventional extreme gradient boost (XGB), random forest, and extra tree algorithms are deployed with a balanced and imbalanced dataset. To produce our model in a trustworthy way, an explainable AI is applied. The techniques are (1) local interpretable model-agnostic explanations (LIME) and (2) Shapley additive explanations (SHAP). Additionally, ELI5 is utilized to inspect the feature’s importance. Finally, XGB outperformed and obtained an AUC of 0.98, which is optimal compared to existing AI systems. |
| first_indexed | 2024-03-11T10:05:02Z |
| format | Article |
| id | doaj.art-8366ff21bf84442a99e1da296ecf4c7a |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T10:05:02Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-8366ff21bf84442a99e1da296ecf4c7a2023-11-16T15:09:56ZengMDPI AGElectronics2079-92922022-12-011211510.3390/electronics12010015Explainable AI to Predict Male Fertility Using Extreme Gradient Boosting Algorithm with SMOTEDebasmita GhoshRoy0Parvez Ahmad Alvi1KC Santosh2School of Automation, Banasthali Vidyapith, Banasthali 304022, IndiaDepartment of Physics, Banasthali Vidyapith, Banasthali 304022, IndiaApplied AI Research Lab, Department of Computer Science, University of South Dakota, Vermillion, SD 57069, USAInfertility is a common problem across the world. Infertility distribution due to male factors ranges from 40% to 50%. Existing artificial intelligence (AI) systems are not often human interpretable. Further, clinicians are unaware of how data analytical tools make decisions, and as a result, they have limited exposure to healthcare. Using explainable AI tools makes AI systems transparent and traceable, enhancing users’ trust and confidence in decision-making. The main contribution of this study is to introduce an explainable model for investigating male fertility prediction. Nine features related to lifestyle and environmental factors are utilized to develop a male fertility prediction model. Five AI tools, namely support vector machine, adaptive boosting, conventional extreme gradient boost (XGB), random forest, and extra tree algorithms are deployed with a balanced and imbalanced dataset. To produce our model in a trustworthy way, an explainable AI is applied. The techniques are (1) local interpretable model-agnostic explanations (LIME) and (2) Shapley additive explanations (SHAP). Additionally, ELI5 is utilized to inspect the feature’s importance. Finally, XGB outperformed and obtained an AUC of 0.98, which is optimal compared to existing AI systems.https://www.mdpi.com/2079-9292/12/1/15explainability techniquesextreme gradient boosting (XGB)SMOTEmale fertility |
| spellingShingle | Debasmita GhoshRoy Parvez Ahmad Alvi KC Santosh Explainable AI to Predict Male Fertility Using Extreme Gradient Boosting Algorithm with SMOTE Electronics explainability techniques extreme gradient boosting (XGB) SMOTE male fertility |
| title | Explainable AI to Predict Male Fertility Using Extreme Gradient Boosting Algorithm with SMOTE |
| title_full | Explainable AI to Predict Male Fertility Using Extreme Gradient Boosting Algorithm with SMOTE |
| title_fullStr | Explainable AI to Predict Male Fertility Using Extreme Gradient Boosting Algorithm with SMOTE |
| title_full_unstemmed | Explainable AI to Predict Male Fertility Using Extreme Gradient Boosting Algorithm with SMOTE |
| title_short | Explainable AI to Predict Male Fertility Using Extreme Gradient Boosting Algorithm with SMOTE |
| title_sort | explainable ai to predict male fertility using extreme gradient boosting algorithm with smote |
| topic | explainability techniques extreme gradient boosting (XGB) SMOTE male fertility |
| url | https://www.mdpi.com/2079-9292/12/1/15 |
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